CN115830872A - Method for judging influence of accident on bicycle use elasticity - Google Patents
Method for judging influence of accident on bicycle use elasticity Download PDFInfo
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Abstract
The invention discloses a method for judging the influence of accident occurrence on the use elasticity of bicycles, which comprises the steps of collecting accident sample information, dividing time units according to the time of the accident occurrence, dividing reasonable buffer areas according to the accident occurrence places, collecting bicycle station information in the buffer areas, counting the bicycle use amount of each driving station in each time unit, and further counting the bicycle use amount of the buffer areas; according to the influence factors of the buffer area, combining the bicycle usage amount of the buffer area to construct a bicycle usage amount elastic model; and (4) applying a bicycle usage amount elastic model, and judging the influence of the variation on the use of the bicycle according to the influence coefficient. The influence of various influence factors on the bicycle use elasticity is analyzed on the basis of the occurrence of the accidents by calculating the bicycle use amount change in the buffer area in unit time. The method can better instruct traffic management departments how to carry out bicycle scheduling efficiently when traffic accidents happen.
Description
Technical Field
The invention relates to the technical field of traffic safety and public bicycles, in particular to a method for judging the influence of an accident on the use elasticity of a bicycle.
Background
In order to relieve urban traffic jam and improve urban traffic operation efficiency, governments in various places actively promote the use of public bicycles. In order to improve the use of the bicycle, the bicycle is put in according to the increase or decrease of the use amount of the bicycle stations. Related patents, such as CN101038685A city bicycle sharing management system, just study the unified management of public bicycles. However, for the occurrence of an uncertain event such as a traffic accident, the influence of the bicycle usage amount on the surrounding bicycle stations is rarely studied, and in the prior art, the bicycle usage condition of the place where the uncertain event occurs is not considered to be related to a plurality of objective factors. At present, aiming at the occurrence of uncertain events, relevant factors can be comprehensively considered, the influence of bicycle use elasticity is judged, and the problem that the bicycle scheduling reaches the most reasonable utilization rate is urgently needed to be solved.
Disclosure of Invention
The invention aims to: the method for judging the influence of the accident on the elasticity of the bicycle is provided, and the influence of the accident on the elasticity of the bicycle is accurately evaluated by dividing an influence buffer area and setting unit time and considering influence factors.
In order to achieve the purpose, the invention provides the following technical scheme: a method for judging the influence of an accident on the elasticity of a bicycle comprises the following steps:
s1, acquiring sample information related to each accident occurrence time and place in a target area;
s2, presetting an accident occurrence time period based on the time of the accident occurrence in the sample information, and dividing the accident occurrence time period into time units;
s3, presetting an accident occurrence buffer area according to the accident occurrence place in the sample information, and collecting the bicycle station information in the buffer area;
s4, according to the time units in the step S2 and the bicycle station information obtained in the step S3, the bicycle usage amount of each driving station in each time unit is counted, and the bicycle usage amount of the buffer area is further counted;
s5, acquiring influence factors of the buffer area, and constructing a bicycle usage elastic model by combining the bicycle usage of the buffer area;
and S6, applying the bicycle usage elastic model, and judging the influence of each variable quantity on the use of the bicycle according to the influence coefficient.
Further, in the step S2, the dividing the time unit specifically includes: based on the time of the accident, the time unit of G hours before and after the accident is divided every t minutes, which is as follows:
G i ＝{t 1 …t n }，
where i is the number of the accident sample.
Further, in the step S3, the preset accident occurrence buffer area specifically includes: the accident occurrence place is taken as the center of a circle, the influence buffer areas of the accident are respectively established by taking the preset distance as the radius, the bicycle station information in the accident buffer areas is correspondingly collected,
n i ＝{d 1 …d k }
wherein ni A predetermined distance buffer for the accident i, d k Is a bicycle station in the buffer zone, where k>1。
Further, in the foregoing step S4, the bicycle usage amount of the buffer area is counted as follows:
wherein ,Q(dk ) The bicycle usage amount of each station in each time unit.
Further, the step S5 is to specifically collect the influencing factors: road network density K ti Economic GDP ti Population P ti Number of bus stops D ti Bicycle usage M combined with buffer zone ti And constructing a bicycle usage elastic model according to the following formula:
wherein ,
is an influencing factor; ε is the model error term, α i For the regression coefficient of each influencing factor, beta 0 Is a constant term of the model;
wherein C is the variance and covariance matrix of the random parameters, j is the number of the random parameters,
refers to other random and uncorrelated variables.
Further, the foregoing S6 specifically is: and (3) obtaining an influence coefficient alpha by applying the bicycle usage elastic model, wherein if the alpha is a positive number, the influence factor corresponding to the alpha has a positive influence on the bicycle usage, and otherwise, the influence factor is a negative influence.
Further, in the method for judging the elastic influence of the accident on the bicycle, the accident buffer zone is established by taking the accident occurrence place as the center of a circle and taking 400 meters as the radius when the accident occurrence buffer zone is preset.
In another aspect, the present invention provides a system for determining an impact of an accident on a bicycle usage elasticity, comprising:
a sample acquisition module configured to perform the following acts: acquiring sample information related to the occurrence time and place of each accident in a target area;
a time-slicing module configured to perform the following actions: presetting an accident occurrence time period based on the time of the accident occurrence in the sample information, and dividing the accident occurrence time period into time units;
a site information acquisition module configured to perform the following actions: presetting an accident occurrence buffer area according to the accident occurrence place in the sample information, and collecting the bicycle station information in the buffer area;
a bicycle usage statistics module configured to perform the following actions: according to the time units obtained by the time division module and the bicycle station information obtained by the station information obtaining module, the bicycle usage amount of each driving station in each time unit is counted, and the bicycle usage amount of the buffer area is further counted;
a bicycle usage elasticity model building module configured to perform the following actions: acquiring influence factors of the buffer area, and constructing a bicycle usage elastic model by combining the bicycle usage of the buffer area;
a bicycle use condition determination module configured to perform the following actions: and (4) applying a bicycle usage amount elastic model, and judging the influence of each influence factor on the bicycle usage according to the influence coefficient.
Compared with the prior art, the invention has the following beneficial effects: different unit times are set by determining the occurrence time of the traffic accident, an influence buffer area is established by taking a reasonable preset distance as a radius accident occurrence place as a core, and the influence of the accident occurrence on the bicycle use elasticity is analyzed by calculating the bicycle use amount in the buffer area in unit time. The method can better instruct traffic management departments how to carry out bicycle scheduling efficiently when traffic accidents happen.
Drawings
FIG. 1 is a method flow diagram of one embodiment of the present invention.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
Aspects of the invention are described herein with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the invention are not limited to those illustrated in the drawings. It is to be understood that the invention is capable of implementation in any of the numerous concepts and embodiments described hereinabove or described in the following detailed description, since the disclosed concepts and embodiments are not limited to any embodiment. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
As shown in fig. 1, a method for determining the influence of an accident on the elasticity of a bicycle comprises the following steps:
s1, acquiring sample information related to each accident occurrence time and place in a target area;
s2, presetting an accident occurrence time period based on the time of the accident occurrence in the sample information, and dividing the accident occurrence time period into time units, wherein the time units are specifically as follows: based on the time of the accident, performing time unit segmentation on G hours before and after the accident by taking t minutes as a unit, wherein the time unit segmentation is as follows:
G i ＝{t 1 …t n }，
where i is the number of the accident sample.
S3, presetting an accident occurrence buffer area according to the accident occurrence place in the sample information, and collecting the bicycle station information in the buffer area; the preset accident occurrence buffer area specifically comprises the following steps: the accident occurrence place is taken as the center of a circle, the influence buffer areas of the accident are respectively established by taking 400 meters as the radius, the bicycle station information in the accident buffer areas is correspondingly collected,
n i ＝{d 1 …d k }
wherein ni A predetermined distance buffer for the accident i, d k Is a bicycle station in the buffer zone, where k>1。
The bicycle station information is shown in table 1:
TABLE 1
And S4, according to the time units in the S2 and the bicycle station information obtained in the S3, counting the bicycle usage amount of each driving station in each time unit, and further counting the bicycle usage amount of the buffer area. The bicycle usage of the buffer area is counted as follows:
wherein ,Q(dk ) The bicycle usage amount of each driving station in each time unit.
S5, collecting influence factors: road network density K ti Economical GDP ti Population P ti Number of bus stops D ti Bicycle usage M combined with buffer zone ti And constructing a bicycle usage elastic model according to the following formula:
wherein ,xi Is an influencing factor;
wherein C is the variance and covariance matrix of the random parameters, j is the number of random parameters,
refers to other random and uncorrelated variables.
The influence factor statistics are shown in table 2:
TABLE 2
| Site | AADT | P | GDP | K | D |
| F 1 | AADT 1 | P 1 | GDP 1 | K 1 | D 1 |
| F 2 | AADT 2 | P 2 | GDP 2 | K 2 | D 2 |
| ～ | ～ | ～ | ～ | ～ | ～ |
| F i | AADT i | P i | GDP i | K i | D i |
And S6, applying the bicycle usage elastic model to judge the influence of the influence factors on the bicycle usage in unit time after the accident occurs, wherein if the influence coefficient alpha is regular, the influence factors have positive influence on the bicycle usage, and otherwise, the influence factors have negative influence on the bicycle usage. The influence of the factors can be used for carrying out targeted strategy to reduce the influence of the accident on the stability of the bicycle system.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.
Claims (8)
1. A method for judging the influence of an accident on the use elasticity of a bicycle is characterized by comprising the following steps:
s1, acquiring sample information related to each accident occurrence time and place in a target area;
s2, presetting an accident occurrence time period based on the time of the accident occurrence in the sample information, and dividing the accident occurrence time period into time units;
s3, presetting an accident occurrence buffer area according to the accident occurrence place in the sample information, and collecting the bicycle station information in the buffer area;
s4, according to the time units in the step S2 and the bicycle station information obtained in the step S3, the bicycle usage amount of each driving station in each time unit is counted, and the bicycle usage amount of the buffer area is further counted;
s5, acquiring influence factors of the buffer area, and constructing a bicycle usage elastic model by combining the bicycle usage of the buffer area;
and S6, applying the bicycle usage elastic model, and judging the influence of each influence factor on the use of the bicycle according to the influence coefficient.
2. The method for determining the impact of an accident on the use flexibility of a bicycle according to claim 1, wherein in step S2, the time division unit is specifically: based on the time of the accident, performing time unit segmentation on G hours before and after the accident by taking t minutes as a unit, wherein the time unit segmentation is as follows:
G i ＝{t 1 …t n }，
where i is the number of the accident sample.
3. The method according to claim 1, wherein the step S3 of presetting the accident occurrence buffer area specifically comprises: the accident occurrence place is taken as the center of a circle, the preset distance is taken as the radius to establish the accident influence buffer area, the bicycle station information in the accident buffer area is correspondingly collected,
n i ＝{d 1 …d k }
wherein ni A predetermined distance buffer for the accident i, d k Is a bicycle station in the buffer zone, where k>1。
4. The method as claimed in claim 3, wherein the step S4 is performed by counting the bicycle usage of the buffer area according to the following formula:
wherein ,Q(dk ) The bicycle usage amount of each driving station in each time unit.
5. The method for determining the influence of the accident on the bicycle usage elasticity according to claim 4, wherein the step S5 is to collect the influence factors: road network density K ti Economical GDP ti Population P ti Number of bus stops D ti Bicycle usage M combined with buffer zone ti And constructing a bicycle usage elastic model according to the following formula:
wherein ,
is an influencing factor; ε is the model error term, α i As a regression coefficient, beta, for each influencing factor 0 Is a constant term of the model;
wherein C is the variance and covariance matrix of the random parameters, j is the number of the random parameters,
refers to other random and uncorrelated variables.
6. The method for determining the impact of an accident on the resilience of a bicycle according to claim 5, wherein S6 is specifically: and obtaining an influence coefficient alpha by applying the bicycle usage elastic model, wherein if the alpha is a positive number, the influence factor corresponding to the alpha has a positive influence on the bicycle usage, and otherwise, the influence factor has a negative influence.
7. The method as claimed in claim 3, wherein the accident buffering area is created with the accident occurring place as a center and the radius of 400 m as a radius when the accident buffering area is preset.
8. A system for determining the impact of an accident on the resiliency of use of a bicycle, comprising:
a sample acquisition module configured to perform the following acts: acquiring sample information related to the occurrence time and place of each accident in a target area;
a time-slicing module configured to perform the following actions: presetting an accident occurrence time period based on the time of the accident occurrence in the sample information, and dividing the accident occurrence time period into time units;
a site information acquisition module configured to perform the following actions: presetting an accident occurrence buffer area according to the place of the accident occurrence in the sample information, and collecting the information of the bicycle stations in the buffer area;
a bicycle usage statistics module configured to perform the following actions: according to the time units obtained by the time division module and the bicycle station information obtained by the station information obtaining module, the bicycle usage amount of each driving station in each time unit is counted, and the bicycle usage amount of the buffer area is further counted;
a bicycle usage elasticity model building module configured to perform the following actions: acquiring influence factors of the buffer area, and constructing a bicycle usage elastic model by combining the bicycle usage of the buffer area;
a bicycle use condition determination module configured to perform the following actions: and (4) applying the bicycle usage amount elastic model, and judging the influence of each influence factor on the bicycle usage according to the influence coefficient.
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