CN112580951A - Urban ground bus operation monitoring key index screening method based on passenger trip - Google Patents
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Abstract
The invention provides a passenger trip-based urban ground bus operation monitoring key index screening method, which comprises the following steps of: the method for analyzing the composition and the initial index of the urban ground public transport operation monitoring system comprises the following steps: analyzing the composition and the main body of the urban ground bus operation monitoring system, analyzing factors influencing the trip selection of bus passengers, and then generating an initial index set for urban ground bus operation monitoring; carrying out quantitative analysis on an initial index set of urban ground bus operation monitoring; respectively carrying out data processing and computational analysis on the two types of indexes to respectively obtain an objectivity index analysis result and a subjectivity index analysis result; and integrating the objective index analysis result and the subjective index analysis result, establishing an accumulated prospect theoretical model, then establishing a characteristic engineering-based urban ground bus operation monitoring key index determination model, inputting the calculated values of all indexes as a model, and outputting the steady-state passenger flow of the bus interval.
Description
Technical Field
The invention relates to the technical field of urban traffic, in particular to a method for screening urban ground bus operation monitoring key indexes based on passenger travel.
Background
The determination of the key indexes of the urban ground bus operation monitoring is beneficial to the discovery of self-deficiency of bus operation enterprises, and provides powerful grippers for operation adjustment of the bus operation enterprises, so that the bus operation efficiency is improved, the service level and attraction of buses are improved, and the traffic jam problem is favorably alleviated. On the other hand, the public transportation financial subsidy resources provided by the government can be reasonably configured according to key public transportation operation service key indexes, so that the reasonable configuration of the resources is realized, and the contradiction between supervision and supervision between the government and public transportation operation enterprises is reconciled, so that the urban ground public transportation system can be stably operated. And finally, the method is beneficial to improving the quality of public transport service and the trip experience of passengers, and the passengers become the maximum beneficiaries, thereby generating extremely strong social and economic benefits.
Currently, with the development of social information technology, the informatization level of the public transportation industry is continuously improved, the electronic data monitoring capability of a public transportation system is also continuously improved, and a plurality of automatic monitoring and collecting systems, such as a public transportation card swiping system, a GPS system, an energy consumption monitoring system and the like, appear. The automatic acquisition system can record bus running data and passenger flow data in real time, generates massive data resources and provides conditions for real-time and normalized monitoring and analysis in the bus industry.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
1. the ground public traffic service monitoring index system proposed at present is huge and complicated, lacks key points, and has high implementation cost, long process and poor effect.
2. Related ground public transportation service monitoring indexes are determined by experience, subjectivity is strong, most of the proposed monitoring indexes are qualitative indexes, quantization is difficult, landing implementation is difficult, indexes irrelevant to improvement of passenger trip experience may exist in an index system, and therefore supervision efficiency is low and supervision effect is limited.
3. The existing index system cannot perform key index research from a microscopic application scene, does not distinguish different application scenes, and does not meet the requirements of dynamic and expandability of urban ground bus operation monitoring. The method is difficult to be combined with actual supervision work, and the guidance value is lacked.
4. In the past, a discrete selection model, a logit model, a Probit model and a structural equation model are mostly adopted, all of the above methods belong to parameter methods, the relationship among variables needs to be set in advance, and the applicability is not strong.
Disclosure of Invention
The object of the present invention is to solve at least one of the technical drawbacks mentioned.
Therefore, the invention aims to provide a method for screening urban ground bus operation monitoring key indexes based on passenger travel.
In order to achieve the above object, an embodiment of the present invention provides a method for screening key indicators of urban ground bus operation monitoring based on passenger travel, including the following steps:
step S1, analyzing the composition and initial index of the urban ground public transportation operation monitoring system, including: analyzing the composition and the main body of the urban ground bus operation monitoring system, analyzing factors influencing the trip selection of bus passengers, and then generating an initial index set for urban ground bus operation monitoring;
step S2, carrying out quantitative analysis on an initial index set of the urban ground bus operation monitoring, wherein the initial index set comprises two types of indexes: (1) an index that can be directly calculated using objective data; (2) subjective indicators that are difficult to quantify with objective data;
step S3, respectively carrying out data processing and calculation analysis on the two types of indexes in the step S2 to respectively obtain an objectivity index analysis result and a subjectivity index analysis result;
and step S4, integrating the objective index analysis result and the subjective index analysis result, constructing an accumulated foreground theoretical model, constructing a characteristic engineering-based urban ground bus operation monitoring key index determination model, inputting the calculated values of all indexes as a model, and outputting the steady-state passenger flow of the bus interval.
Further, in step S1, the factors influencing the travel selection of the bus passenger include: environmental conditions, personal characteristics of travelers, travel characteristics, travel psychology and traffic service characteristics.
Further, in step S3, the data processing and calculation analysis of the index directly calculated by using the objective data includes the following steps: carry out data preprocessing and analysis to objective resident's trip data and public transit operating data, include: importing a database, cleaning data, integrating multi-source data, matching and calculating data, reducing data and calculating indexes, and sorting the data into a format of model input.
Further, in step S3, the data set processing and the calculation analysis of the subjectivity index that is difficult to be quantified by objective data includes the following steps: collecting and analyzing the index language value by using questionnaire data; the language values are then quantized using the cloud model.
Further, the collecting and analyzing of the index language values by using the questionnaire data includes questionnaire design, questionnaire distribution and recovery, questionnaire data entry and cleaning, and integration with objective data.
Further, the quantified value of each index is determined by inputting the resident travel RP survey and SP survey data into a cloud model that processes uncertainty information.
Further, the quantizing the language value by using the cloud model includes: and 4, mapping the comment value interval, calculating comment value expectation and calculating index value expectation.
Further, in the step S4, the building of the accumulated foreground theoretical model includes the following steps: and inputting an objective calculation value of the index and outputting the subjective value of the passenger on the index.
Further, (1) determining an index;
(2) selecting a reference point to calculate the gain or loss;
(3) calculating a value of a cost function;
(4) calculating a subjective frequency weight value;
(5) and calculating the subjective value of the index.
Further, in the step S4, the constructing of the characteristic engineering-based determination model for the key index of urban ground bus operation monitoring includes the following steps: determining key indexes based on a wrapping method in feature selection engineering, selecting a sequence backward selection algorithm to search feature subsets, selecting prediction errors of a generalized regression neural network as subset evaluation criteria, and constructing a determination model of key indexes of urban ground bus operation monitoring
According to the method for screening the urban ground bus operation monitoring key indexes based on passenger travel, the key indexes of urban ground bus operation monitoring with prominent emphasis are scientifically determined by combining multi-level travel scenes in reality and combining subjective perception data of passengers and objective bus operation data from the aspect of bus passenger travel selection, and the problems of low efficiency and poor effect of the existing urban ground bus operation monitoring are solved.
The invention determines the key indexes of urban ground bus operation monitoring in multiple levels and multiple dimensions under different scenes, adds new content to the bus operation service monitoring research under the current urban ground bus operation monitoring, provides a new angle, and the determination of the key indexes has dynamic property and expandability and can be more effectively applied in practice. On the other hand, the determination of the key indexes avoids the defects of macro and general index systems in the past, has prominent emphasis and has guiding and reference significance. And finally, the passenger subjective data and the bus operation objective data are combined to research the urban ground bus operation monitoring key indexes, so that the defect of one-sidedness in the previous research is overcome, and the research result is more reliable and convincing. Meanwhile, a foundation is laid for the subsequent normalized monitoring research and analysis of the urban ground bus running condition.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a passenger trip-based urban ground bus operation monitoring key index screening method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of factors influencing bus passenger travel selection, according to an embodiment of the present invention;
FIG. 3 is a flowchart of a generalized recurrent neural network model training and test modeling process according to an embodiment of the present invention;
FIG. 4 is a technical roadmap for a multi-model integrated prediction algorithm according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
As shown in fig. 1 and 4, the method for screening the urban ground bus operation monitoring key indexes for passenger travel according to the embodiment of the invention includes the following steps:
step S1, analyzing the composition and initial index of the urban ground public transportation operation monitoring system, including: the composition and the main body of the urban ground public transport operation monitoring system are analyzed, and a foundation is laid for subsequent content analysis. Then, the trip decision-making behaviors of travelers are researched through documents, factors influencing the trip selection of bus passengers are analyzed, and an initial index set determined by the urban ground bus operation monitoring key indexes is formed.
In the embodiment of the invention, the factors influencing the travel selection of the bus passengers comprise: environmental conditions, personal characteristics of travelers, travel characteristics, travel psychology and traffic service characteristics.
Specifically, the research object of the invention is the travel selection behavior of the bus passenger, not the travel selection behavior of urban residents in all transportation modes, and considering that three aspects of urban economic culture development conditions, traffic policies, roads and infrastructure do not cause the selection difference of the passenger among bus travel routes, so that for the travel selection of the bus passenger, only the five influencing factors of environmental conditions, personal characteristics of travelers, travel characteristics, travel psychology and traffic service characteristics are considered, as shown in fig. 2.
Step S2, carrying out quantitative analysis on an initial index set of urban ground bus operation monitoring, wherein the initial index set comprises two types of indexes: (1) an index that can be directly calculated using objective data; (2) subjective indicators that are difficult to quantify with objective data.
And step S3, respectively carrying out data processing and calculation analysis on the two types of indexes in the step S2 to respectively obtain an objectivity index analysis result and a subjectivity index analysis result.
In the step, objective resident travel data and bus running data are processed and analyzed, data are imported into a database, data are cleaned, multi-source data are integrated, data are reduced, index calculation is carried out, and the data are arranged into a format of model input. And secondly, collecting and analyzing questionnaire data, wherein the questionnaire data comprises questionnaire design, questionnaire distribution and recovery, questionnaire data entry and cleaning and objective data integration.
(1) The data processing and calculation analysis of the index directly calculated by objective data includes the following steps: carry out data preprocessing and analysis to objective resident's trip data and public transit operating data, include: importing a database, cleaning data, integrating multi-source data, matching and calculating data, reducing data and calculating indexes, and sorting the data into a format of model input.
The first type of index is subjected to index calculation and analysis by depending on resident travel data and bus running data, each index is defined, and the calculation mode of each index is determined by analysis. The bus route real-time arrival data comprises bus IC card data, bus real-time arrival data, bus route basic data and bus route stop data. Summary of data content and data source are shown in table 1.
TABLE 1 summary of bus data content and data Source
(2) The data set processing and computational analysis of subjective indicators that are difficult to quantify with objective data, comprising the steps of: collecting and analyzing the index language value by using questionnaire data; the language values are then quantized using the cloud model.
And collecting and analyzing the index language value by using questionnaire data, wherein the index language value comprises questionnaire design, questionnaire distribution and recovery, questionnaire data entry and cleaning and objective data integration. The quantitative value of each index is determined by inputting resident trip RP survey and SP survey data into a cloud model for processing uncertainty information.
Quantifying the language values using a cloud model, comprising: and 4, mapping the comment value interval, calculating comment value expectation and calculating index value expectation.
The second category of indexes is quantified by means of resident travel rp (reclaimed preference) survey and sp (stateful preference) survey data, which are input into a cloud model for processing uncertainty information to determine a quantified value of each index. The second type of data specifically comprises subjective perception scores of the bus passengers on various indexes of the taken bus lines. The examinee needs to evaluate the 7 qualitative indexes and select the satisfaction degree of the qualitative indexes. The survey index is shown in table 2 and includes three latent variables and seven observed variables.
And then, data processing and analysis, namely processing and analyzing objective resident travel data and bus running data, including data import into a database, data cleaning, multi-source data integration, data reduction and index calculation, and sorting the data into a format of model input. And secondly, collecting and analyzing questionnaire data, wherein the questionnaire data comprises questionnaire design, questionnaire distribution and recovery, questionnaire data entry and cleaning and objective data integration.
TABLE 2 subjective perceptual survey index for SP surveys
And step S4, integrating objective index analysis results and subjective index analysis results, constructing an accumulated prospect theoretical model, constructing a characteristic engineering-based urban ground bus operation monitoring key index determination model, inputting calculated values of all indexes as a model, and outputting steady-state passenger flow of a bus interval.
In this step, a qualitative index quantitative model based on survey data is constructed. The passenger riding subjective perception data obtained by SP and RP investigation exist in a qualitative form of language concept, so that the passenger traveling experience and the subjective evaluation on the ground public transport operation system are visually depicted. However, since such qualitative indexes are difficult to be directly input as a key index model, it is necessary to perform a quantitative process on the qualitative indexes, introduce a cloud model, and use the index values after the quantitative process as a part of the input of the key index model.
In the step, a model is constructed based on objective index foreground value calculation of resident travel data. Considering that passengers are influenced by psychological factors during travel selection, an accumulation prospect theory is introduced, an objective calculation value of an index is input into an accumulation prospect theory model, and the subjective value of the passenger on the index is output, so that the method is closer to a travel selection scene in the real world and the relation between a public transport service characteristic index and a steady-state passenger flow is more reliably depicted.
Specifically, the method for constructing the accumulated foreground theoretical model comprises the following steps: and inputting an objective calculation value of the index and outputting the subjective value of the passenger on the index.
(1) Determining an index;
(2) selecting a reference point to calculate the gain or loss;
(3) calculating a value of a cost function;
(4) calculating a subjective frequency weight value;
(5) and calculating the subjective value of the index.
And finally, constructing a model for determining the key indexes of the urban ground bus operation monitoring based on the characteristic engineering, determining the key indexes based on a wrapping method in the characteristic selection engineering, selecting a sequence backward selection algorithm to search the characteristic subsets, selecting the prediction error of the generalized regression neural network as a subset evaluation criterion, constructing the model for determining the key indexes of the urban ground bus operation monitoring, taking the calculated values of all indexes as model input, and taking the steady-state passenger flow of the bus interval as output.
Specifically, the method for establishing the urban ground public transport operation monitoring key index determination model based on the characteristic engineering comprises the following steps: determining key indexes based on a wrapping method in feature selection engineering, selecting a sequence backward selection algorithm to search feature subsets, selecting prediction errors of a generalized regression neural network as subset evaluation criteria, and constructing a determination model of the key indexes of urban ground bus operation monitoring.
The qualitative index and the quantitative index are respectively processed through the steps, and the processed indexes are an initial index set for monitoring the operation of the urban ground buses. In order to determine the key indexes of the urban ground bus operation monitoring, a key index determination model is constructed, and the processed initial indexes are input into the sequence backward-generalized regression neural network key index determination model to obtain a key index determination result. FIG. 4 is a flowchart of a generalized recurrent neural network model training and testing modeling.
As shown in fig. 4, the training and testing modeling process of the generalized regression neural network model includes the following steps:
(1) generating a feature set;
(2) inputting a training sample;
(3) preprocessing data;
(4) inputting a smoothing factor;
(5) training a neural network;
(6) judging whether the smoothness is the optimal parameter or not, and if so, executing the step (7); otherwise, returning to the step (4);
(7) storing the training result;
(8) inputting a test sample, and performing model prediction;
(9) a prediction error is obtained.
To sum up, the method for screening the key indexes of the urban ground bus operation monitoring based on passenger travel in the embodiment of the invention realizes the following technical scheme and technical effects:
1. the passenger riding subjective perception data obtained by SP and RP investigation exist in a qualitative form of language concept, so that the passenger traveling experience and the subjective evaluation on the ground public transport operation system are visually depicted. And moreover, a cloud model is introduced, and the index value after the quantitative processing is used as a part of the input of the key index model, so that the problem that the qualitative index is difficult to directly serve as the input of the key index model is solved.
2. In the real travel selection of the passengers, the passengers are influenced by subjective psychological perception, and finally the travel selection is promoted under the combined action of the objective index values and the subjective perception of the passengers. Therefore, when the influence of the quantitative indexes on the passenger travel selection is considered, the objective value is taken as a basis, the risk attitude preference of the passenger on the indexes is added, and the two aspects are combined to describe the value of the influence factor influencing the passenger travel selection. Therefore, the accumulated foreground theory is introduced into the further calculation of objective index values, the foreground values of all indexes are used as key indexes of the indexes to determine the model input value, so that the travel selection behavior of passengers is more truly depicted, and the result determined by the key indexes is more accurate and reliable.
3. The method comprises the steps of establishing an urban ground bus operation monitoring key index determination model based on a characteristic project, determining key indexes based on a wrapping method in a characteristic selection project, selecting a sequence backward selection algorithm to search a characteristic subset, selecting a prediction error of a generalized regression neural network as a subset evaluation criterion, establishing the urban ground bus operation monitoring key index determination model, taking calculated values of all indexes as model input, and taking a bus interval steady-state passenger flow as output.
According to the method for screening the urban ground bus operation monitoring key indexes based on passenger travel, the key indexes of urban ground bus operation monitoring with prominent emphasis are scientifically determined by combining multi-level travel scenes in reality and combining subjective perception data of passengers and objective bus operation data from the aspect of bus passenger travel selection, and the problems of low efficiency and poor effect of the existing urban ground bus operation monitoring are solved.
The invention determines the key indexes of urban ground bus operation monitoring in multiple levels and multiple dimensions under different scenes, adds new content to the bus operation service monitoring research under the current urban ground bus operation monitoring, provides a new angle, and the determination of the key indexes has dynamic property and expandability and can be more effectively applied in practice. On the other hand, the determination of the key indexes avoids the defects of macro and general index systems in the past, has prominent emphasis and has guiding and reference significance. And finally, the passenger subjective data and the bus operation objective data are combined to research the urban ground bus operation monitoring key indexes, so that the defect of one-sidedness in the previous research is overcome, and the research result is more reliable and convincing. Meanwhile, a foundation is laid for the subsequent normalized monitoring research and analysis of the urban ground bus running condition.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A passenger trip-based urban ground public transport operation monitoring key index screening method is characterized by comprising the following steps:
step S1, analyzing the composition and initial index of the urban ground public transportation operation monitoring system, including: analyzing the composition and the main body of the urban ground bus operation monitoring system, analyzing factors influencing the trip selection of bus passengers, and then generating an initial index set for urban ground bus operation monitoring;
step S2, carrying out quantitative analysis on an initial index set of the urban ground bus operation monitoring, wherein the initial index set comprises two types of indexes: (1) an index that can be directly calculated using objective data; (2) subjective indicators that are difficult to quantify with objective data;
step S3, respectively carrying out data processing and calculation analysis on the two types of indexes in the step S2 to respectively obtain an objectivity index analysis result and a subjectivity index analysis result;
and step S4, integrating the objective index analysis result and the subjective index analysis result, constructing an accumulated foreground theoretical model, constructing a characteristic engineering-based urban ground bus operation monitoring key index determination model, inputting the calculated values of all indexes as a model, and outputting the steady-state passenger flow of the bus interval.
2. The passenger travel-based urban ground bus operation monitoring key index screening method according to claim 1, wherein in step S1, the factors influencing bus passenger travel selection include: environmental conditions, personal characteristics of travelers, travel characteristics, travel psychology and traffic service characteristics.
3. The passenger travel-based urban ground bus operation monitoring key index screening method according to claim 1, wherein in step S3, data processing and computational analysis are performed on the index directly calculated by objective data, comprising the following steps: carry out data preprocessing and analysis to objective resident's trip data and public transit operating data, include: importing a database, cleaning data, integrating multi-source data, matching and calculating data, reducing data and calculating indexes, and sorting the data into a format of model input.
4. The passenger travel-based urban ground bus operation monitoring key index screening method according to claim 1, wherein in step S3, the method for processing and calculating and analyzing the subjective index difficult to be quantified by objective data comprises the following steps: collecting and analyzing the index language value by using questionnaire data; the language values are then quantized using the cloud model.
5. The passenger trip-based urban ground bus operation monitoring key index screening method according to claim 4, wherein the index language values are collected and analyzed by using questionnaire data, and the method comprises questionnaire design, questionnaire distribution and recovery, questionnaire data entry and cleaning, and objective data integration.
6. The passenger trip-based urban ground bus operation monitoring key index screening method according to claim 4, characterized in that the quantitative value of each index is determined by means of inputting the RP survey data and the SP survey data of resident trips into a cloud model for processing uncertainty information.
7. The passenger trip-based urban ground public transportation operation monitoring key index screening method according to claim 4, wherein the quantizing the language value by using the cloud model comprises: and 4, mapping the comment value interval, calculating comment value expectation and calculating index value expectation.
8. The passenger trip-based urban ground bus operation monitoring key index screening method according to claim 1, wherein in the step S4, the building of the accumulated foreground theoretical model comprises the following steps: and inputting an objective calculation value of the index and outputting the subjective value of the passenger on the index.
9. The passenger trip-based urban ground bus operation monitoring key index screening method according to claim 8,
(1) determining an index;
(2) selecting a reference point to calculate the gain or loss;
(3) calculating a value of a cost function;
(4) calculating a subjective frequency weight value;
(5) and calculating the subjective value of the index.
10. The passenger trip-based urban ground bus operation monitoring key index screening method according to claim 1, wherein in the step S4, the constructing of the characteristic engineering-based urban ground bus operation monitoring key index determination model comprises the following steps: determining key indexes based on a wrapping method in feature selection engineering, selecting a sequence backward selection algorithm to search feature subsets, selecting prediction errors of a generalized regression neural network as subset evaluation criteria, and constructing a determination model of the key indexes of urban ground bus operation monitoring.
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