CN112950092B - Street riding performance evaluation method, device and storage medium based on track data - Google Patents
Street riding performance evaluation method, device and storage medium based on track data Download PDFInfo
- Publication number
- CN112950092B CN112950092B CN202110418097.8A CN202110418097A CN112950092B CN 112950092 B CN112950092 B CN 112950092B CN 202110418097 A CN202110418097 A CN 202110418097A CN 112950092 B CN112950092 B CN 112950092B
- Authority
- CN
- China
- Prior art keywords
- street
- riding
- evaluation index
- target street
- score
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 156
- 238000000611 regression analysis Methods 0.000 claims abstract description 17
- 239000011159 matrix material Substances 0.000 claims description 22
- 238000000034 method Methods 0.000 claims description 17
- 238000001303 quality assessment method Methods 0.000 claims 1
- 238000004891 communication Methods 0.000 description 4
- 238000013507 mapping Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a street riding ability evaluation method based on track data, which comprises the following steps: acquiring an evaluation index of a target street for riding a shared bicycle, wherein the evaluation index comprises the acquisition of the riding requirement of the target street, the attribute of the target street, bicycle supply of the target street and the riding frequency of the shared bicycle on the target street; carrying out regression analysis on each evaluation index to obtain the score of each evaluation index; and determining the ridable result of the target street on the shared bicycle according to each score. The invention also discloses a street riding ability evaluation device and a storage medium based on the track data. The invention improves the riding determination efficiency of the urban street.
Description
Technical Field
The invention relates to the technical field of sharing bicycles, in particular to a street riding ability evaluation method, a street riding ability evaluation device and a storage medium based on track data.
Background
As a novel green transportation travel tool, the sharing bicycle not only effectively solves the problem of last kilometer of resident travel, but also promotes the slow-going system to return to the field of vision of people.
Furthermore, riding of the sharing bicycle also requires consideration of the problem of profitability. For some street environments, where the operator may apply for sharing a bicycle in multiple arrangements of such streets, while others are not, the operator may arrange or not share a bicycle in fewer arrangements of such streets. To screen city streets suitable for shared bicycle riding, ridable studies of city streets may be conducted.
Currently, a research on the riding of urban streets is based on the fact that personnel collect street information and share riding data of a bicycle on the street, and the manually collected information is not necessarily accurate and takes a long time, so that the riding determination efficiency of the urban streets is low.
Disclosure of Invention
The main purpose of the invention is to provide a street riding ability assessment method, a street riding ability assessment device and a storage medium based on track data, aiming at the problem that the riding determination efficiency of urban streets is low.
In order to achieve the above object, the present invention provides a street riding ability evaluation method based on track data, the street riding ability evaluation method based on track data comprising the steps of:
Acquiring an evaluation index of a target street for riding a shared bicycle, wherein the evaluation index comprises the acquisition of the riding requirement of the target street, the attribute of the target street, bicycle supply of the target street and the riding frequency of the shared bicycle on the target street;
carrying out regression analysis on each evaluation index to obtain the score of each evaluation index;
And determining the ridable result of the target street on the shared bicycle according to each score.
In one embodiment, the step of determining the ridable outcome of the target street to the shared bicycle based on each of the scores comprises:
determining the score grade of the score on the evaluation index corresponding to the score;
and determining the riding result of the target street on the shared bicycle according to each score grade.
In one embodiment, the step of determining the ridable outcome of the target street to the shared bicycle based on each of the score levels comprises:
determining the position of each evaluation index in a preset matrix;
Adding the score grade corresponding to the evaluation index to the position of the evaluation index in the preset matrix to obtain an evaluation matrix;
and determining a ridable result of the target street on the shared bicycle according to the evaluation matrix.
In one embodiment, the step of performing regression analysis on each of the evaluation indexes to obtain the score of each of the evaluation indexes includes:
Carrying out regression analysis on each evaluation index according to a regression model to obtain the weight corresponding to each evaluation index, and determining the coefficient of each evaluation index according to the weight;
and determining an initial score of each evaluation index, and determining a score corresponding to the evaluation index according to the initial score corresponding to the evaluation index and the coefficient.
In an embodiment, the evaluation index includes a riding frequency, and the step of obtaining the evaluation index that the target street can ride on the sharing bicycle includes:
Acquiring riding tracks of each sharing bicycle in a preset time period;
and determining the number of riding tracks passing through the target street, and determining the riding frequency according to the number and the preset time period.
In an embodiment, the evaluation index includes a riding requirement, and the step of obtaining an evaluation index that the target street can ride on the sharing bicycle includes:
determining a distance between the target street and a site and a length of the target street;
And determining the riding requirement according to the distance and the length.
In an embodiment, the evaluation index comprises an attribute of the target street, the attribute comprising a slope of the target street, a green coverage of the target street, a road quality of the target street, a size parameter of a riding track of the target street, a size parameter of a parking area of the target street to the shared bicycle, a number of the parking areas, and a safety parameter of the target street.
In order to achieve the above object, the present invention also provides a street cyclicity evaluation device based on track data, which is characterized in that the street cyclicity evaluation device based on track data includes a memory, a processor, and a determining program stored in the memory and executable on the processor, wherein the determining program when executed by the processor implements the steps of the street cyclicity evaluation method based on track data as described above.
To achieve the above object, the present invention also provides a storage medium storing a determination program which, when executed by a processor, performs the steps of the street-rideability evaluation method based on trajectory data as described above.
The invention provides a street riding performance evaluation method, a street riding performance evaluation device and a storage medium based on track data. According to the method and the device for determining the riding results of the city streets on the shared bicycle, the riding results of the city streets on the shared bicycle are automatically determined, the data are not required to be acquired manually, and the riding determination efficiency of the city streets is improved.
Drawings
FIG. 1 is a schematic hardware diagram of a street riding ability evaluation device based on track data according to an embodiment of the present invention;
FIG. 2 is a flowchart of a street riding ability evaluation method based on track data according to a first embodiment of the present invention;
FIG. 3 is a flowchart of a street riding ability evaluation method based on track data according to a second embodiment of the present invention;
fig. 4 is a flowchart of a third embodiment of a street riding ability evaluation method based on track data according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The main solutions of the embodiments of the present invention are: acquiring an evaluation index of a target street for riding a shared bicycle, wherein the evaluation index comprises the acquisition of the riding requirement of the target street, the attribute of the target street, bicycle supply of the target street and the riding frequency of the shared bicycle on the target street; carrying out regression analysis on each evaluation index to obtain the score of each evaluation index; and determining the ridable result of the target street on the shared bicycle according to each score.
According to the method and the device for determining the riding results of the city streets on the shared bicycle, the riding results of the city streets on the shared bicycle are automatically determined, the data are not required to be acquired manually, and the riding determination efficiency of the city streets is improved.
Referring to fig. 1, fig. 1 is a schematic hardware structure diagram of a street riding ability evaluation apparatus based on track data according to an embodiment of the present invention.
As shown in fig. 1, an embodiment of the present invention relates to a street riding ability evaluation device based on track data, where the street riding ability evaluation device based on track data may include: a processor 101, such as a CPU, a communication bus 102, and a memory 103. Wherein the communication bus 102 is used to enable connected communication between these components. The memory 103 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 103 may alternatively be a storage device separate from the aforementioned processor 101. It will be appreciated by those skilled in the art that the structure shown in FIG. 1 does not constitute a limitation of the street riding ability assessment apparatus based on trajectory data, and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, a determination program may be included in the memory 103 as one type of computer storage medium.
In the apparatus shown in fig. 1, the processor 101 may be configured to call a determination program stored in the memory 103 and perform the following operations:
Acquiring an evaluation index of a target street for riding a shared bicycle, wherein the evaluation index comprises the acquisition of the riding requirement of the target street, the attribute of the target street, bicycle supply of the target street and the riding frequency of the shared bicycle on the target street;
carrying out regression analysis on each evaluation index to obtain the score of each evaluation index;
And determining the ridable result of the target street on the shared bicycle according to each score.
In one embodiment, the processor 101 may call a determination program stored in the memory 103, and further perform the following operations:
determining the score grade of the score on the evaluation index corresponding to the score;
and determining the riding result of the target street on the shared bicycle according to each score grade.
In one embodiment, the processor 101 may call a determination program stored in the memory 103, and further perform the following operations:
determining the position of each evaluation index in a preset matrix;
Adding the score grade corresponding to the evaluation index to the position of the evaluation index in the preset matrix to obtain an evaluation matrix;
and determining a ridable result of the target street on the shared bicycle according to the evaluation matrix.
In one embodiment, the processor 101 may call a determination program stored in the memory 103, and further perform the following operations:
Carrying out regression analysis on each evaluation index according to a regression model to obtain the weight corresponding to each evaluation index, and determining the coefficient of each evaluation index according to the weight;
and determining an initial score of each evaluation index, and determining a score corresponding to the evaluation index according to the initial score corresponding to the evaluation index and the coefficient.
In one embodiment, the processor 101 may call a determination program stored in the memory 103, and further perform the following operations:
Acquiring riding tracks of each sharing bicycle in a preset time period;
and determining the number of riding tracks passing through the target street, and determining the riding frequency according to the number and the preset time period.
In one embodiment, the processor 101 may call a determination program stored in the memory 103, and further perform the following operations:
determining a distance between the target street and a site and a length of the target street;
And determining the riding requirement according to the distance and the length.
In one embodiment, the processor 101 may call a determination program stored in the memory 103, and further perform the following operations:
The evaluation index comprises attributes of the target street, wherein the attributes comprise gradient of the target street, greening coverage rate of the target street, road quality of the target street and size parameters of riding channels of the target street, size parameters of parking areas of the target street to a shared bicycle, number of parking areas and safety parameters of the target street.
According to the scheme, the device acquires the riding requirements of the target street, the attribute of the target street, the bicycle supply of the target street, the riding frequency of the shared bicycle on the target street and other evaluation indexes, carries out regression analysis on each evaluation index to obtain the score of each evaluation index, and then determines the riding result of the target street on the shared bicycle according to each score. According to the method and the device for determining the riding results of the city streets on the shared bicycle, the riding results of the city streets on the shared bicycle are automatically determined, the data are not required to be acquired manually, and the riding determination efficiency of the city streets is improved.
Based on the hardware architecture of the street riding ability evaluation device based on the track data, an embodiment of the street riding ability evaluation method based on the track data is provided.
Referring to fig. 2, fig. 2 is a first embodiment of a street cyclicity evaluation method based on track data according to the present invention, the street cyclicity evaluation method based on track data includes the following steps:
Step S10, acquiring an evaluation index of a target street for riding a shared bicycle, wherein the evaluation index comprises the steps of acquiring the riding requirement of the target street, the attribute of the target street, bicycle supply of the target street and the riding frequency of the shared bicycle on the target street;
in the present embodiment, the execution subject is a street-rideability evaluation device based on trajectory data, and for convenience of description, the following device refers to a street-rideability evaluation device based on trajectory data.
The user can input the street which needs to be subjected to the riding study of the shared bicycle in the device, and the street is the target street. After the device acquires the input target street, the evaluation index of the target street for riding the shared bicycle is acquired. The evaluation index comprises the steps of obtaining the riding requirement of a target street, the attribute of the target street, the bicycle supply of the target street and the riding frequency of the sharing bicycle on the target street.
Specifically, the device may be regarded as a background server of the shared bicycle, or the device is in communication connection with the background server, and obtains a riding track of each shared bicycle within a preset time period, where the preset time period is a time period set by a user, for example, the preset time period defaults to the last week, the last month, and the like. The riding track comprises a travel path of the shared bicycle and street names corresponding to each position of the travel track, the device screens the riding track of the route target street, so that the number of the riding tracks of the route target street is obtained, and then the riding frequency is determined through the number and a preset time period. For example, if the preset time period is the last week and the number is 50, the riding frequency of the target street is 50 times/week.
The evaluation index also includes riding demand. The riding demand characterizes the demand of people to ride a shared bicycle on a target street, which can be determined by the distance between the target street and the site and the length of the target street. For example, if a bus station or a subway station is on a target street, people can take a vehicle without walking for a long distance, that is, without riding. For another example, if the target street is long, then the person walks to the destination for a long time, and thus needs to ride on the target street. Thus, the device may determine the riding demand based on the distance between the target street and the site, the length of the target street. Specifically, a weight corresponding to the distance and a weight corresponding to the length may be set, and a score may be obtained by performing a weighted calculation based on the weight, the distance and the length, and if the score is greater than a threshold, there is a riding demand, and if the score is less than the threshold, there is no riding demand. Of course, the riding demand may be directly characterized by a score.
The evaluation index further comprises attributes of the target street, wherein the attributes comprise gradient of the target street, greening coverage rate of the target street, road quality of the target street and size parameters of riding channels of the target street, size parameters of parking areas of the target street to the shared bicycle, number of parking areas and safety parameters of the target street. Specifically, the size parameter of the parking area is the area of the parking area, the parking area refers to the area where the street is used for dividing the shared bicycle, and the larger the area is, the larger the supporting force of the target street on the shared bicycle is; similarly, the greater the number of parking areas, the greater the support of the target street to the sharing bicycle. Furthermore, the safety parameter refers to the proportion of lost and damaged shared bicycles on the target street, i.e. the safety parameter refers to the degree of damage to shared bicycles by residents on the target street, and the safety parameter may be the ratio of the number of lost shared bicycles and the total number of damaged shared bicycles in the target street divided by the total number of shared bicycles on the street.
The evaluation index also includes a single vehicle supply of the target street, and the single vehicle supply can be divided into a dynamic supply and a static supply. The bicycle dynamic supply may extract the amount of bicycle used on the street for a certain cumulative period of time through the trajectory data. The static supply of the bicycle can be performed by counting the number of bicycles, the GPS of which is unchanged in a certain time period.
Step S20, carrying out regression analysis on each evaluation index to obtain the score of each evaluation index;
After obtaining each evaluation index, the device carries out regression analysis on each evaluation index, namely, carries out operation on each evaluation index based on a regression algorithm to obtain the influence degree of each evaluation index on riding, and further obtains the weight corresponding to each evaluation index based on the influence degree. Each evaluation index has a corresponding score, the device stores the mapping relation between the type of the evaluation index and the score, the device obtains the corresponding mapping relation based on each evaluation index, and then the initial score of the evaluation index is substituted into the mapping relation to obtain the score of each evaluation index. The initial score may be a sum of factors in the evaluation index, for example, the evaluation index includes an attribute of the target street, the attribute includes a gradient of the target street, a greening coverage rate of the target street, a road quality of the target street, and a size parameter of a riding path of the target street, the gradient, the greening coverage rate, the road quality of the target street, and the size parameter of the riding path of the target street correspond to a small score, and the larger the gradient, the smaller the score; the larger the greening coverage rate, the road quality of the target street and the size parameter of the riding path of the target street, the larger the corresponding score value is, and the sum of the score values is the initial score.
And step S30, determining the ridable result of the target street on the shared bicycle according to each score.
After the score of each evaluation index is determined, the device can determine the riding result of the target street on the shared bicycle according to each score. For example, each score and the corresponding weight are weighted to obtain a total score, and if the total score is greater than a preset score, the riding result is high; if the total score is less than or equal to the predetermined score, the ridable result is low.
In the technical scheme provided by the embodiment, the device acquires the riding requirement of the target street, the attribute of the target street, the bicycle supply of the target street, the riding frequency of the shared bicycle on the target street and other evaluation indexes, carries out regression analysis on each evaluation index to obtain the score of each evaluation index, and then determines the riding result of the target street on the shared bicycle according to each score. According to the method and the device for determining the riding results of the city streets on the shared bicycle, the riding results of the city streets on the shared bicycle are automatically determined, the data are not required to be acquired manually, and the riding determination efficiency of the city streets is improved.
Referring to fig. 3, fig. 3 is a second embodiment of the street riding ability evaluation method based on track data according to the present invention, based on the first embodiment, the step S30 includes:
Step S31, determining the score grade of the score on the evaluation index corresponding to the score;
And step S32, determining the riding result of the target street on the shared bicycle according to each score grade.
In this embodiment, each evaluation index has a corresponding level, and the level determination manners of different evaluation indexes are different. After the device obtains the score corresponding to each evaluation index, the score grade of the score on the evaluation index corresponding to the score can be determined according to the grade determination mode of the evaluation index, and the score grade can be a high grade and a low grade. After each score grade is determined, the device can determine the riding result of the target street on the shared bicycle according to each score grade.
In addition, the device may determine the ridable outcome through a matrix of individual score levels. Specifically, each rating index has a fixed position in the matrix, so the device determines the position of each rating index in the preset matrix, then adds the score grade corresponding to the rating index to the position of the rating index in the preset matrix to obtain the rating matrix, and obtains a riding result according to the rating evidence. For example, the position of the riding demand in the matrix is the first position, the position of the bicycle supply in the matrix is the second position, the position of the attribute in the matrix is the third position, the position of the riding frequency in the matrix is the fourth position, and if the evaluation matrix is (high, low, high) and (high ), the riding result is rideable.
According to the technical scheme provided by the embodiment, the device determines the score grade of the score on the evaluation index corresponding to the score, and accurately determines the riding result of the target street on the shared bicycle according to each score grade.
Referring to fig. 4, fig. 4 is a third embodiment of the street riding ability evaluation method based on track data according to the present invention, based on the first or second embodiment, the step S20 includes:
S21, carrying out regression analysis on each evaluation index according to a regression model to obtain a weight corresponding to each evaluation index, and determining a coefficient of the evaluation index according to the weight;
step S22, determining an initial score of each evaluation index, and determining a score corresponding to the evaluation index according to the initial score corresponding to the evaluation index and the coefficient.
In this embodiment, a regression model is set in the device, and the device may perform regression analysis on each evaluation index through the regression model, so as to obtain a weight corresponding to each evaluation index, where the weight may be converted into a coefficient corresponding to the evaluation index. The device obtains the initial score of each evaluation index, namely the score corresponding to the evaluation index can be obtained according to the initial frequency division and the coefficient.
It should be noted that the regression model may be trained by a training sample, where one training sample is the weight of each evaluation index of a street.
In this embodiment, the device performs rapid regression analysis on each evaluation index through the regression model, so that the riding determination efficiency of the city street is further improved.
The present invention also provides a street cyclicity evaluation device based on track data, which includes a memory, a processor, and a determining program stored in the memory and executable on the processor, wherein the determining program is executed by the processor to implement the steps of the street cyclicity evaluation method based on track data according to the above embodiment.
The present invention also provides a storage medium storing a determination program which, when executed by a processor, performs the steps of the street-rideability evaluation method based on trajectory data as described in the above embodiments.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (7)
1. A street cyclicity evaluation method based on track data, characterized in that the street cyclicity evaluation method based on track data comprises the following steps:
Acquiring an evaluation index of a target street for riding a shared bicycle, wherein the evaluation index comprises the acquisition of the riding requirement of the target street, the attribute of the target street, bicycle supply of the target street and the riding frequency of the shared bicycle on the target street;
carrying out regression analysis on each evaluation index to obtain the score of each evaluation index;
determining a ridable result of the target street on the shared bicycle according to each score;
Wherein, the step of determining the ridable result of the target street to the shared bicycle according to each score comprises the following steps:
determining the score grade of the score on the evaluation index corresponding to the score;
determining a riding result of the target street on the shared bicycle according to each score grade;
Wherein, the step of determining the ridable result of the target street to the shared bicycle according to each score grade comprises the following steps:
determining the position of each evaluation index in a preset matrix;
Adding the score grade corresponding to the evaluation index to the position of the evaluation index in the preset matrix to obtain an evaluation matrix;
and determining a ridable result of the target street on the shared bicycle according to the evaluation matrix.
2. The street riding quality assessment method based on track data according to claim 1, wherein the step of performing regression analysis on the respective evaluation indexes to obtain a score of each of the evaluation indexes comprises:
Carrying out regression analysis on each evaluation index according to a regression model to obtain the weight corresponding to each evaluation index, and determining the coefficient of each evaluation index according to the weight;
and determining an initial score of each evaluation index, and determining a score corresponding to the evaluation index according to the initial score corresponding to the evaluation index and the coefficient.
3. The street cyclicity evaluation method based on track data as set forth in claim 1 or 2, wherein the evaluation index includes a riding frequency, and the step of obtaining the evaluation index of the target street for the shared bicycle comprises:
Acquiring riding tracks of each sharing bicycle in a preset time period;
and determining the number of riding tracks passing through the target street, and determining the riding frequency according to the number and the preset time period.
4. The method for evaluating the street riding quality based on track data according to claim 1 or 2, wherein the evaluation index comprises a riding demand, and the step of obtaining the evaluation index of the target street riding the shared bicycle comprises the following steps:
determining a distance between the target street and a site and a length of the target street;
And determining the riding requirement according to the distance and the length.
5. The track data-based street cyclicity assessment method according to claim 1 or 2, wherein the assessment index comprises an attribute of a target street, the attribute comprising a gradient of the target street, a green coverage of the target street, a road quality of the target street, a size parameter of a riding track of the target street, a size parameter of a parking area of the target street to a shared bicycle, a number of the parking areas, and a safety parameter of the target street.
6. A street cyclicity evaluation device based on track data, characterized in that it comprises a memory, a processor and a determination program stored in the memory and executable on the processor, which determination program, when executed by the processor, implements the steps of the street cyclicity evaluation method based on track data according to any one of claims 1-5.
7. A storage medium storing a determination program which, when executed by a processor, performs the steps of the street-rideability assessment method based on trajectory data as claimed in any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110418097.8A CN112950092B (en) | 2021-04-19 | 2021-04-19 | Street riding performance evaluation method, device and storage medium based on track data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110418097.8A CN112950092B (en) | 2021-04-19 | 2021-04-19 | Street riding performance evaluation method, device and storage medium based on track data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112950092A CN112950092A (en) | 2021-06-11 |
| CN112950092B true CN112950092B (en) | 2024-06-25 |
Family
ID=76232910
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110418097.8A Active CN112950092B (en) | 2021-04-19 | 2021-04-19 | Street riding performance evaluation method, device and storage medium based on track data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112950092B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108009734A (en) * | 2017-12-12 | 2018-05-08 | 东峡大通（北京）管理咨询有限公司 | Vehicle launches appraisal procedure and machinable medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109544690B (en) * | 2018-10-18 | 2020-09-22 | 华南理工大学 | Method, system and storage medium for identifying influence factors of travel of shared bicycle |
| CN112597402B (en) * | 2020-12-17 | 2022-11-18 | 东南大学 | Rail transit transfer shared single-vehicle influence area defining method based on multi-source data |
-
2021
- 2021-04-19 CN CN202110418097.8A patent/CN112950092B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108009734A (en) * | 2017-12-12 | 2018-05-08 | 东峡大通（北京）管理咨询有限公司 | Vehicle launches appraisal procedure and machinable medium |
Non-Patent Citations (1)
| Title |
|---|
| "单车上的老北京---北京老城区骑行行为影响因素研究";李煜茜等;北京规划建设;20180515(第3期);正文第6-12页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112950092A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8630791B2 (en) | Dynamic route guidance | |
| US8989999B2 (en) | Vehicle range analysis using driving environment information with optional continuous averaging | |
| US20160063516A1 (en) | Methods and apparatus to estimate commercial characteristics based on geospatial data | |
| CN108256714A (en) | A kind of wheelpath real-time risk assessment model based on car networking big data | |
| JP5815718B2 (en) | Method for determining and verifying navigation priority settings using probe data | |
| CN104320789B (en) | Game theory-based vehicle networking RSU optimal configuration method | |
| CN104599002B (en) | Method and equipment for predicting order value | |
| CN106248096B (en) | The acquisition methods and device of road network weight | |
| CN106227884B (en) | A kind of recommended method of calling a taxi online based on collaborative filtering | |
| CN109726852A (en) | Based on route planning method, device, terminal and the medium for improving ant group algorithm | |
| US20150006255A1 (en) | Determining demographic data | |
| CN112556717B (en) | Travel mode screening method and travel route recommending method and device | |
| JP5273106B2 (en) | Traffic flow calculation device and program | |
| US20150005007A1 (en) | Displaying demographic data | |
| CN116050935B (en) | Method and device for determining information of biodiversity priority protection area | |
| CN109993971A (en) | A method of promoting traffic accident site locating accuracy | |
| CN112950092B (en) | Street riding performance evaluation method, device and storage medium based on track data | |
| CN111723835A (en) | Vehicle movement track distinguishing method and device and electronic equipment | |
| CN109961344B (en) | Automobile service site selection method and device | |
| CN104318319A (en) | Multi-agent urban expansion simulation method based on land acquisition process competition result | |
| Steenberghen et al. | Support study on data collection and analysis of active modes use and infrastructure in Europe | |
| CN113469455A (en) | Prediction method and device of endurance data | |
| CN111832599B (en) | Gas station prediction method based on machine learning random forest | |
| CN116502960A (en) | Station area TOD development potential evaluation method, system and electronic equipment | |
| Celiński et al. | Sustainable development of the transport system through rationalization of transport tasks using a specialised travel planner |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |