CN113361749A - Bus express route planning method, device, equipment and medium based on principal component analysis - Google Patents
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Abstract
The invention discloses a bus express route planning method, a device, equipment and a medium based on principal component analysis, wherein the method comprises the following steps: carrying out statistical analysis according to the travel data and the travel scheme of the target bus standard to obtain a daily travel scheme data table of the target bus; performing principal component analysis on the travel scheme in the data table to generate a principal component of the travel scheme of the target bus; carrying out statistical analysis on the standard travel data and the principal component travel scheme of the target bus to obtain a principal component travel scheme data table of the target bus per hour; performing principal component analysis on the time periods in the data table to generate a travel time period principal component of the target bus; and planning a bus express line according to the travel scheme principal component and the travel time period principal component. According to the bus express line planning method provided by the embodiment of the disclosure, principal component analysis can be performed on multidimensional data, and bus express line construction can be guided more scientifically and efficiently.
Description
Technical Field
The invention relates to the technical field of public transport line optimization, in particular to a bus express route planning method, device, equipment and medium based on principal component analysis.
Background
The public transport is used as an important carrier for urban public green travel, has strong advantages in public travel service scenes in view of the wide service range, and is the first choice for public travel. However, with the increase of travel service modes such as rail transit, shared bicycle and the like, and the congestion of urban road traffic, the public transport travel amount is continuously decreased, so that the public travel efficiency is improved in a fast line mode, and the method is a preferred method for improving the public transport service.
In the prior art, the bus stop passenger flow analysis is performed on the basis of bus card swiping data on a macroscopic level, fine differentiation is not performed on the whole bus trip, and rapid bus route construction decision is mainly based on subjectivity.
Disclosure of Invention
The embodiment of the disclosure provides a bus express route planning method, device, equipment and medium based on principal component analysis. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present disclosure provides a bus route planning method based on principal component analysis, including:
carrying out statistical analysis according to the travel data and the travel scheme of the target bus standard to obtain a daily travel scheme data table of the target bus;
performing principal component analysis on the travel scheme in the data table to generate a principal component of the travel scheme of the target bus;
carrying out statistical analysis on the standard travel data and the principal component travel scheme of the target bus to obtain a principal component travel scheme data table of the target bus per hour;
performing principal component analysis on the time periods in the data table to generate a travel time period principal component of the target bus;
and planning the bus express line according to the travel scheme principal component and the travel time period principal component.
In an optional embodiment, before performing statistical analysis according to the travel data and the travel plan of the target bus standard, the method further includes:
acquiring bus trip influence factors, wherein the influence factors comprise weather, date types and traffic road conditions;
carrying out layered modeling on the influence factors, and calculating the weight corresponding to each layer of influence factors;
when the weight of the influence factor at the bottommost layer is smaller than a preset threshold value, determining that the influence factor has smaller influence on bus trip;
and filtering the travel data of the target bus in a preset time period according to factors having small influence on the travel of the bus to obtain standard travel data.
In an optional embodiment, the statistical analysis is performed according to the travel data and the travel scheme of the target bus standard to obtain a daily travel scheme data table of the target bus, including:
counting a travel scheme of the target bus according to the operation route of the target bus;
counting the average number of people going out per day in each going out scheme according to standard going out data;
and obtaining a daily trip scheme data table of the target bus according to the trip scheme, the trip date and the average number of trips.
In an optional embodiment, the performing principal component analysis on the travel plan in the data table to generate a principal component of the travel plan of the target bus includes:
performing principal component analysis on the trip scheme in the data table to obtain a first total variance interpretation table;
extracting the principal component according to the first total variance interpretation table;
carrying out load calculation on the extracted main components and calculating a comprehensive score;
and taking the preset number of principal components with higher comprehensive scores as the principal components of the trip scheme of the target bus.
In an optional embodiment, the statistical analysis is performed on the standard travel data and the principal component travel scheme of the target bus to obtain a principal component travel scheme data table of the target bus per hour, including:
counting the average number of people going out per hour of each principal component going out scheme according to standard going out data;
and obtaining a principal component travel scheme data table of the target bus per hour according to the principal component travel scheme, the travel time period and the average number of people who travel.
In an optional embodiment, the performing principal component analysis on the time period in the data table to generate a travel time period principal component of the target bus includes:
performing principal component analysis on the time periods in the data table to obtain a second total variance interpretation table;
extracting the principal component according to a second total variance interpretation table;
carrying out sum product calculation of load ratio on the extracted main components, and calculating a comprehensive fraction;
and taking the preset number of principal components with higher comprehensive scores as the principal components of the travel time period of the target bus.
In an optional embodiment, planning a bus express according to a travel scheme principal component and a travel time period principal component includes:
taking the main components of the travel scheme as running stations of the bus express lines;
and taking the main component of the travel time period as the running time of the bus express line.
In a second aspect, an embodiment of the present disclosure provides a bus route planning apparatus based on principal component analysis, including:
the first statistical module is used for carrying out statistical analysis according to the travel data and the travel scheme of the target bus standard to obtain a daily travel scheme data table of the target bus;
the first principal component analysis module is used for carrying out principal component analysis on the travel scheme in the data table to generate a principal component of the travel scheme of the target bus;
the second statistical module is used for performing statistical analysis on the standard travel data and the principal component travel scheme of the target bus to obtain a principal component travel scheme data table of the target bus in each hour;
the second principal component analysis module is used for performing principal component analysis on the time periods in the data table to generate the principal components of the travel time periods of the target buses;
and the planning module is used for planning the bus express line according to the travel scheme principal component and the travel time period principal component.
In a third aspect, the embodiment of the present disclosure provides a bus route planning device based on principal component analysis, including a processor and a memory storing program instructions, where the processor is configured to execute the bus route planning method based on principal component analysis provided in the foregoing embodiment when executing the program instructions.
In a fourth aspect, the disclosed embodiments provide a computer-readable medium, on which computer-readable instructions are stored, where the computer-readable instructions are executable by a processor to implement a bus route planning method based on principal component analysis provided in the foregoing embodiments.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
the disclosed embodiment provides a bus express line planning method based on principal component analysis, which is based on public bus travel data, eliminates external interference factors by cleaning multi-factor data such as weather, road conditions, holidays and the like, then carries out principal component analysis on the multi-dimensional data, outputs a reasonable stop and time period combination scheme, and guides express line construction more scientifically.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic flow chart diagram illustrating a principal component analysis-based method for bus route planning, according to an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating a principal component analysis-based bus route planning method according to an exemplary embodiment;
FIG. 3 is a schematic diagram illustrating a factor affecting bus travel in accordance with an exemplary embodiment;
FIG. 4 is a diagram illustrating a principal component analysis result according to an exemplary embodiment;
FIG. 5 is a diagram illustrating a load calculation result according to an exemplary embodiment;
FIG. 6 is a diagram illustrating the results of a calculation of a new component value in accordance with an illustrative embodiment;
FIG. 7 is a diagram illustrating a composite score calculation in accordance with an exemplary embodiment;
FIG. 8 is a schematic structural diagram of a bus route planning apparatus based on principal component analysis according to an exemplary embodiment;
FIG. 9 is a schematic diagram illustrating a principal component analysis-based bus route planning apparatus in accordance with an exemplary embodiment;
FIG. 10 is a schematic diagram illustrating a computer storage medium in accordance with an exemplary embodiment.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of systems and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The bus route planning method based on principal component analysis provided by the embodiment of the application will be described in detail below with reference to fig. 1 to 7.
Referring to fig. 1, the method specifically includes the following steps;
s101, carrying out statistical analysis according to the travel data and the travel scheme of the target bus standard to obtain a daily travel scheme data table of the target bus.
In real life, people often receive the influence of factors such as weather, date, road conditions on taking the public transit trip, for example, a large amount of crowds can take the public transit trip on holidays, the student can take the public transit to go home on holidays, selects the public transit trip to give up private car when traffic jams, gives up going out during bad weather, and the like. Therefore, the number of people who travel on the bus is often influenced in special situations.
In a possible implementation manner, before executing step S101, the method further includes acquiring travel data of the target bus standard.
Firstly, bus travel influence factors are obtained, wherein the influence factors comprise weather, date types and traffic road conditions. Fig. 3 is a diagram illustrating factors affecting bus travel according to an exemplary embodiment. As shown in fig. 3, the factors affecting the travel of the bus include weather factors, date type factors and traffic conditions, wherein the weather factors include wind, rain/snow, sand storm, air pollution index, etc., the wind includes below 3, 4-5, 6-7, and above 7, the rain/snow includes small, medium, and large, the sand storm includes floating, blowing, sand storm, and strong sand storm, the air pollution index includes below 100, 100 plus 200, 200 plus 300, and above 300, the date type includes working day, weekend, legal holiday, major activity period, etc., and the traffic conditions include smooth, slow, and congested traffic.
Furthermore, the influence factors are subjected to layered modeling, weight calculation is performed step by step according to a sum-product method, the weight of each layer of influence factors for the previous layer of influence factors is calculated, and finally, the combination weight is obtained. As shown in the following table:
analyzing the weight of each factor, and in a possible implementation mode, determining that the influence factor has less influence on the travel of the bus when the weight of the influence factor at the bottommost layer is less than a preset threshold, wherein the preset threshold can be set by a person skilled in the art according to the actual situation. And then, selecting factors with small influence on the selection of the bus trip by the traveler, constructing a data filtering condition, and filtering the trip data of the target bus in a preset time period to obtain standard trip data. For example, travel data of the target bus within one month is filtered according to data filtering conditions of wind power level 5 and below, light rain/snow and below, floating dust and below, air pollution index 100 and below, and working days, so that relatively standard bus travel data are obtained.
Further, statistical analysis is carried out according to the trip data and the trip scheme of the target bus standard, and a daily trip scheme data table of the target bus is obtained.
Firstly, counting travel schemes of target buses according to an operation route of the target buses, wherein the target buses are 1 bus, and the 1 bus has 10 stops of z1, z2, z3, z4, z5, z6, z7, z8, z9, z10 and the like, sorting all travel schemes of the 1 bus according to an exhaustion method to obtain a travel scheme set F ═ F1, F2, F3, F4, … and F45, and the total number of the travel schemes is 45.
Then, the average number of people who go out every day of each trip scheme is counted according to the standard trip data, and a trip scheme data table of the target bus every day is obtained according to the trip scheme, the trip date and the average number of people who go out. As shown in the following table:
| 1 month and 4 |
1 month and 5 |
1 month and 6 days | …… | |
|
| F1 | 496 | 413 | 353 | …… | 495 |
| F2 | 405 | 469 | 352 | …… | 490 |
| F3 | 494 | 433 | 449 | …… | 408 |
| …… | …… | …… | …… | …… | …… |
| F45 | 444 | 408 | 484 | …… | 352 |
S102, performing principal component analysis on the number of people going out in the daily going-out scheme in the data table to generate the principal component of the going-out scheme of the target bus.
In a possible implementation manner, a Principal Component Analysis (PCA) is a multivariate statistical Analysis method that linearly transforms a plurality of variables to select a smaller number of important variables, and is also called Principal Component Analysis. Through principal component analysis, important bus stops can be selected from the data sheet, and therefore the bus express line construction is scientifically guided.
Firstly, principal component analysis is carried out on a trip scheme in a data table to obtain a first total variance interpretation table. In a possible implementation mode, a sps software is adopted to perform principal component analysis on a daily trip scheme data table of a target bus to obtain a first total variance interpretation table. Fig. 4 is a diagram illustrating a principal component analysis result according to an exemplary embodiment, as shown in fig. 4, which cuts the analysis results of the first 20 principal components, and in the embodiment of the present disclosure, the principal components refer to new variables derived from the number of sunrise pedestrians in the trip plan data table.
From the first total variance interpretation table, principal components are extracted, as shown in fig. 4, the first 6 variables have been 70.797% accumulated, and the eigenvalues are >1, so the first 6 variables can be chosen as the extracted principal components, whose initial characteristic roots: λ 1 ═ 2.522, λ 2 ═ 2.500, λ 3 ═ 2.433, λ 4 ═ 2.383, λ 5 ═ 2.261, λ 6 ═ 2.059; principal component contribution rate: r 1-12.608%, r 2-12.502%, r 3-12.167%, r 4-11.917%, r 5-11.307%, and r 6-10.297%.
Alternatively, one skilled in the art may select a plurality of main variables in the analysis result that are ranked in the front row by itself, and the disclosed embodiment does not limit the number of variables, for example, select variables with cumulative value of more than 80%.
Further, load calculation is performed on the extracted principal components to obtain the ratio of each original variable in the new 6 principal components. FIG. 5 is a diagram illustrating a load calculation result according to an exemplary embodiment. By performing the load calculation, the interpretability of each principal component can be enhanced.
Further, according to the ratio of the original variables in each principal component, a new principal component value can be obtained, as shown in fig. 6, which is a new calculated principal component value and serves as a new attribute of each travel plan in terms of the number of specific travel persons.
Further, a composite score is calculated from the new principal component values calculated in fig. 6, and the principal components are sorted according to the composite score. For example, according to Z ═ Σ ri*FACiCalculating a principal component composite score, wherein riRepresenting the degree of contribution of principal component, FACiRepresenting the new principal component value calculated in the previous step. The calculated composite score is shown in fig. 7.
Finally, taking a preset number of principal components with higher comprehensive scores as principal components of the travel scheme of the target bus, and in an exemplary scene, taking the travel scheme s1 with the top three ranked travel schemes as 1 bus as { F ═ F }15,F40,F8Get the bus line express stop Zk＝{Z1,Z9,Z2,Z8,Z7}。
According to the steps, several important bus stops can be selected from a large number of bus stops through a principal component analysis method, and guidance opinions are provided for the construction of bus express lines.
S103, carrying out statistical analysis on the standard travel data and the principal component travel scheme of the target bus to obtain a principal component travel scheme data table of the target bus in each hour.
In a possible implementation, after several important bus stops are selected, the operation time of the bus needs to be analyzed. For example, the 1-way bus operation time is 6:00-21:00, so the data obtained in the steps are processed according to a small-time-interval and trip scheme s1 to obtain the hourly bus scheme trip data.
Firstly, counting the average number of people who go out per hour for each principal component trip plan according to standard trip data, and then obtaining a principal component trip plan data table per hour for a target bus according to the principal component trip plan, trip time periods and the average number of people who go out. As shown in the following table:
and S104, performing principal component analysis on the time periods in the data table to generate the principal components of the travel time periods of the target buses.
Similarly, the time slots in the data table in step S103 are subjected to principal component analysis, several important time slots are selected, and the construction of the bus express is guided according to the selected important time slots.
In an optional embodiment, the performing principal component analysis on the time period in the data table to generate a travel time period principal component of the target bus includes: and carrying out principal component analysis on the time periods in the data table to obtain a second total variance interpretation table, extracting principal components according to the second total variance interpretation table, carrying out load calculation on the extracted principal components, calculating comprehensive scores, and taking a preset number of principal components with higher comprehensive scores as the principal components of the travel time periods of the target buses.
The principal component analysis method in this step is the same as the principal component analysis method in step S102, and the embodiments of the present disclosure are not explained in detail. After principal component analysis and comprehensive score sorting, the preferential running time of the express line can be 17-18 points, 15-16 points and 13-14 points.
And S105, planning the bus express line according to the travel scheme principal component and the travel time period principal component.
In an optional embodiment, planning a bus express according to a travel scheme principal component and a travel time period principal component includes: and taking the main component of the travel scheme as an operation station of the bus express line and taking the main component of the travel time period as the operation time of the bus express line. In one exemplary scenario, bus stops may be set according to Zk ═ { Z1, Z9, Z2, Z8, Z7 }, and operate between 17-18, 15-16, and 13-14 points.
The public bus trip data is used as a basis, external interference factors are eliminated through multi-factor data cleaning, then main cause analysis is carried out on the multi-dimensional data, a reasonable station and time period combination scheme is output, and express line setting is guided more scientifically.
In order to facilitate understanding of the bus route planning method based on principal component analysis provided in the embodiment of the present application, the following description is made with reference to fig. 2. As shown in fig. 2, the method includes:
firstly, travel data of a target bus are obtained, for example, all travel data of 1 bus in 1 month are obtained, and data collection is achieved.
And further, screening data by combining various influence factors to obtain relatively standard travel data. The influence factors comprise weather factors, date factors and traffic road condition factors, wherein the weather factors comprise wind, rain, snow, sand storms and the like, the date influence factors comprise weekends, holidays, major activities and the like, and the road condition factors comprise smoothness, congestion and the like.
And calculating the weight of each influence factor, wherein the smaller the weight value is, the smaller the influence on the bus trip is, constructing a data screening condition according to the factors with small influence, and screening the bus trip data in a preset time period to obtain relatively standard trip data. For example, travel data of a target bus within one month are screened according to data screening conditions of wind power level 5 and below, light rain/snow and below, floating dust and below, air pollution index 100 and below and working days, and relatively standard bus travel data are obtained.
Further, calculating the average value of the day-level data to obtain a daily trip scheme data table of the target bus. Counting a travel scheme of the target bus according to the operation route of the target bus; counting the average number of people going out per day in each going out scheme according to standard going out data; and obtaining a daily trip scheme data table of the target bus according to the trip scheme, the trip date and the average number of trips.
Further, performing principal component analysis on the trip scheme in the data table to obtain a first total variance interpretation table; extracting the principal component according to the first total variance interpretation table; carrying out load calculation on the extracted main components and calculating a comprehensive score; and taking the preset number of principal components with higher comprehensive scores as the principal components of the trip scheme of the target bus.
And further, calculating the average value of the hourly data to obtain a principal component travel scheme data table of the target bus in each hour. And counting the average number of the trips per hour of each principal component trip scheme according to the standard trip data, and obtaining a principal component trip scheme data table of the target bus per hour according to the principal component trip scheme, the trip time period and the average number of the trips.
And carrying out principal component analysis on the time periods in the data table to generate a travel time period principal component of the target bus, and planning the bus route according to the travel scheme principal component and the travel time period principal component.
According to the bus express line planning method based on principal component analysis, provided by the embodiment of the disclosure, the external interference factors can be eliminated by cleaning multi-factor data such as weather, road conditions, holidays and the like, then principal component analysis is carried out on the multi-dimensional data, a reasonable station and time period combination scheme is output, and express line construction is guided more scientifically.
The embodiment of the present disclosure further provides a bus route planning device based on principal component analysis, where the device is configured to execute the bus route planning method based on principal component analysis of the foregoing embodiment, and as shown in fig. 8, the device includes:
the first statistical module 801 is configured to perform statistical analysis according to the travel data and the travel scheme of the target bus standard to obtain a daily travel scheme data table of the target bus;
the first principal component analysis module 802 is configured to perform principal component analysis on the travel plan in the data table to generate a principal component of the travel plan of the target bus;
the second statistical module 803 is configured to perform statistical analysis on the standard trip data and the principal component trip plan of the target bus to obtain a principal component trip plan data table of the target bus per hour;
the second principal component analysis module 804 is used for performing principal component analysis on the time periods in the data table to generate the principal components of the travel time periods of the target buses;
and the planning module 805 is used for planning the bus express line according to the travel scheme principal component and the travel time period principal component.
It should be noted that, when the bus route planning apparatus based on principal component analysis provided in the foregoing embodiment executes the bus route planning method based on principal component analysis, only the division of the functional modules is illustrated, and in practical application, the function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the embodiments of the bus route planning device based on principal component analysis and the bus route planning method based on principal component analysis provided in the above embodiments belong to the same concept, and details of the implementation process are shown in the method embodiments and are not described herein.
The embodiment of the present disclosure further provides an electronic device corresponding to the principal component analysis-based bus route planning method provided in the foregoing embodiment, so as to execute the principal component analysis-based bus route planning method.
Referring to fig. 9, a schematic diagram of an electronic device provided in some embodiments of the present application is shown. As shown in fig. 9, the electronic apparatus includes: the system comprises a processor 900, a memory 901, a bus 902 and a communication interface 903, wherein the processor 900, the communication interface 903 and the memory 901 are connected through the bus 902; the memory 901 stores a computer program that can be executed on the processor 900, and when the processor 900 executes the computer program, the bus route planning method based on principal component analysis provided by any of the foregoing embodiments of the present application is executed.
The Memory 901 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 903 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The processor 900 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 900. The Processor 900 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 901, and the processor 900 reads the information in the memory 901, and completes the steps of the above method in combination with the hardware thereof.
The electronic equipment provided by the embodiment of the application and the bus route planning method based on principal component analysis provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.
Referring to fig. 10, the computer readable storage medium is an optical disc 1000, and a computer program (i.e., a program product) is stored thereon, and when the computer program is executed by a processor, the method for planning a bus route based on principal component analysis according to any of the foregoing embodiments is executed.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the bus route planning method based on principal component analysis provided by the embodiment of the present application have the same inventive concept, and have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A bus route planning method based on principal component analysis is characterized by comprising the following steps:
carrying out statistical analysis according to the travel data and the travel scheme of the target bus standard to obtain a daily travel scheme data table of the target bus;
performing principal component analysis on the travel scheme in the data table to generate a principal component of the travel scheme of the target bus;
carrying out statistical analysis on the standard travel data and the principal component travel scheme of the target bus to obtain a principal component travel scheme data table of the target bus per hour;
performing principal component analysis on the time periods in the data table to generate a travel time period principal component of the target bus;
and planning a bus express line according to the travel scheme principal component and the travel time period principal component.
2. The method of claim 1, wherein before performing statistical analysis according to the travel data and the travel plan of the target bus standard, the method further comprises:
acquiring bus trip influence factors, wherein the influence factors comprise weather, date types and traffic road conditions;
carrying out layered modeling on the influence factors, and calculating the weight corresponding to each layer of influence factors;
when the weight of the influence factor at the bottommost layer is smaller than a preset threshold value, determining that the influence factor has small influence on bus trip;
and filtering the travel data of the target bus in a preset time period according to factors having small influence on the travel of the bus to obtain standard travel data.
3. The method according to claim 1, wherein the statistical analysis is performed according to the travel data and the travel plan of the target bus standard to obtain a daily travel plan data table of the target bus, and the method comprises the following steps:
counting a travel scheme of the target bus according to the operation route of the target bus;
counting the average number of people going out per day in each going out scheme according to the standard going out data;
and obtaining a daily trip scheme data table of the target bus according to the trip scheme, the trip date and the average trip population.
4. The method according to claim 1, wherein the step of performing principal component analysis on the travel plan in the data table to generate principal components of the travel plan of the target bus comprises:
performing principal component analysis on the trip scheme in the data table to obtain a first total variance interpretation table;
extracting the principal component according to the first total variance interpretation table;
carrying out load calculation on the extracted main components and calculating a comprehensive score;
and taking the preset number of principal components with higher comprehensive scores as the principal components of the trip scheme of the target bus.
5. The method according to claim 1, wherein the statistical analysis is performed on the standard trip data and the principal component trip plan of the target bus to obtain a principal component trip plan data table of the target bus per hour, and the method comprises the following steps:
counting the average number of people who go out per hour in each principal component going out scheme according to the standard going out data;
and obtaining a principal component travel scheme data table of the target bus per hour according to the principal component travel scheme, the travel time period and the average number of people who travel.
6. The method according to claim 1, wherein the step of performing principal component analysis on the time periods in the data table to generate a travel time period principal component of the target bus comprises the following steps:
performing principal component analysis on the time periods in the data table to obtain a second total variance interpretation table;
extracting principal components according to the second total variance interpretation table;
carrying out sum product calculation of load ratio on the extracted main components, and calculating a comprehensive fraction;
and taking the preset number of principal components with higher comprehensive scores as the principal components of the travel time period of the target bus.
7. The method of claim 1, wherein planning a bus route according to the travel plan principal component and the travel time period principal component comprises:
taking the main component of the travel scheme as an operation station of the bus express line;
and taking the travel time period principal component as the running time of the bus express line.
8. The utility model provides a bus express way planning device based on principal component analysis which characterized in that includes:
the first statistical module is used for carrying out statistical analysis according to the travel data and the travel scheme of the target bus standard to obtain a daily travel scheme data table of the target bus;
the first principal component analysis module is used for carrying out principal component analysis on the travel scheme in the data table to generate a travel scheme principal component of the target bus;
the second statistical module is used for performing statistical analysis on the standard trip data and the principal component trip scheme of the target bus to obtain a principal component trip scheme data table of the target bus in each hour;
the second principal component analysis module is used for performing principal component analysis on the time periods in the data table to generate the principal components of the travel time periods of the target buses;
and the planning module is used for planning the bus express line according to the travel scheme principal component and the travel time period principal component.
9. A principal component analysis-based bus route planning apparatus comprising a processor and a memory storing program instructions, the processor being configured to execute the principal component analysis-based bus route planning method according to any one of claims 1 to 7 when executing the program instructions.
10. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement a principal component analysis-based method of bus route planning as claimed in any one of claims 1 to 7.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103606271A (en) * | 2013-11-27 | 2014-02-26 | 大连理工大学 | Method for controlling hybrid power urban buses |
| CN104134105A (en) * | 2014-08-18 | 2014-11-05 | 东南大学 | Public-transit-network layout optimization method |
| US20150199636A1 (en) * | 2014-01-13 | 2015-07-16 | Xerox Corporation | Method and system for performance metric anomaly detection in transportation systems |
| KR101860457B1 (en) * | 2017-04-20 | 2018-05-23 | 주식회사 웨더아이엠씨 | Method for analyzing weather affect and apparatus for executing the method |
| CN108241844A (en) * | 2016-12-27 | 2018-07-03 | 北京文安智能技术股份有限公司 | A kind of public traffice passenger flow statistical method, device and electronic equipment |
| CN112651546A (en) * | 2020-12-10 | 2021-04-13 | 北京北大千方科技有限公司 | Bus route optimization method and system |
| CN112700073A (en) * | 2019-10-22 | 2021-04-23 | 华为技术有限公司 | Bus route planning method and device |
-
2021
- 2021-05-14 CN CN202110529423.2A patent/CN113361749B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103606271A (en) * | 2013-11-27 | 2014-02-26 | 大连理工大学 | Method for controlling hybrid power urban buses |
| US20150199636A1 (en) * | 2014-01-13 | 2015-07-16 | Xerox Corporation | Method and system for performance metric anomaly detection in transportation systems |
| CN104134105A (en) * | 2014-08-18 | 2014-11-05 | 东南大学 | Public-transit-network layout optimization method |
| CN108241844A (en) * | 2016-12-27 | 2018-07-03 | 北京文安智能技术股份有限公司 | A kind of public traffice passenger flow statistical method, device and electronic equipment |
| KR101860457B1 (en) * | 2017-04-20 | 2018-05-23 | 주식회사 웨더아이엠씨 | Method for analyzing weather affect and apparatus for executing the method |
| CN112700073A (en) * | 2019-10-22 | 2021-04-23 | 华为技术有限公司 | Bus route planning method and device |
| CN112651546A (en) * | 2020-12-10 | 2021-04-13 | 北京北大千方科技有限公司 | Bus route optimization method and system |
Non-Patent Citations (2)
| Title |
|---|
| 胡启洲;张卫华;: "基于功效函数的公交线网优化", 交通与计算机, no. 03 * |
| 黄永刚;温惠英;: "城市公交线网规划综合评价方法研究", 城市公共交通, no. 09 * |
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