CN112580951B - Urban ground bus operation monitoring key index screening method based on passenger travel - Google Patents
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Abstract
The invention provides a city ground bus operation monitoring key index screening method based on passenger travel, which comprises the following steps: analyzing the composition and initial index of the urban ground bus operation monitoring system, comprising: analyzing the constitution and the main body of the urban ground bus operation monitoring system, analyzing factors influencing the travel 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 for urban ground bus operation monitoring; respectively carrying out data processing and calculation analysis on the two indexes to respectively obtain an objective index analysis result and a subjective index analysis result; integrating data in a chassis of an objective index analysis result and a subjective index analysis result, constructing an accumulated prospect theoretical model, then constructing a city ground bus operation monitoring key index determination model based on characteristic engineering, taking calculated values of various indexes as a model input, and taking steady-state passenger flow of a bus interval as output.
Description
Technical Field
The invention relates to the technical field of urban traffic, in particular to a screening method for urban ground bus operation monitoring key indexes based on passenger traveling.
Background
The determination of the urban ground bus operation monitoring key index is beneficial to finding the defects of the bus operation enterprises, powerful grippers are provided for the operation adjustment of the bus operation enterprises, the bus operation efficiency is further improved, the service level and the attraction of buses are improved, and the traffic jam problem is relieved. On the other hand, the public transport financial subsidy resource provided by the government can be reasonably configured according to key indexes of key public transport operation service, so that the reasonable configuration of the resource is realized, and the contradiction between the government and public transport operation enterprises about supervision and supervision is reconciled, so that the urban ground public transport system runs stably. And finally, the improvement of the public transportation service quality and the improvement of the traveling experience of passengers are facilitated, the passengers become the biggest beneficiaries, and extremely strong social and economic benefits are generated.
Along 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 are arranged. The automatic acquisition system can record bus running data and passenger flow data in real time, generates massive data resources, and provides conditions for monitoring and analyzing the bus industry in real time and in a normalized mode.
In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:
1. the currently proposed ground bus service monitoring index system is huge and complicated, and lacks of important points, and has high implementation cost, long process and poor effect.
2. The relevant ground bus service monitoring indexes are determined by experience, the subjectivity is strong, the proposed monitoring indexes are mostly qualitative indexes, the quantitative and floor implementation are difficult, indexes which are irrelevant to improving the traveling experience of passengers can exist in an index system, and therefore the supervision efficiency is low and the supervision effect is limited.
3. The conventional index system cannot perform key index research from application scenes at a microscopic level, does not distinguish different application scenes, and does not meet the requirements of urban ground bus operation monitoring dynamics and expandability. It is difficult to combine with the actual supervision work, and lacks guiding value.
4. In the past, discrete selection models, logic models, probit models and structural equation models are adopted, all belong to parameter methods, the relation among variables is required 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.
Therefore, the invention aims to provide a screening method for urban ground bus operation monitoring key indexes based on passenger traveling.
In order to achieve the above purpose, the embodiment of the invention provides a screening method for urban ground bus operation monitoring key indexes based on passenger traveling, comprising the following steps:
step S1, analyzing the composition and initial index of the urban ground bus operation monitoring system, comprising the following steps: analyzing the constitution and the main body of the urban ground bus operation monitoring system, analyzing factors influencing the travel 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 for urban ground bus operation monitoring, wherein the initial index set comprises two types of indexes: (1) an index directly computable with 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 indexes in the step S2 to respectively obtain an objective index analysis result and a subjective index analysis result;
And S4, integrating the objective index analysis result and the subjective index analysis result in the case, constructing an accumulated prospect theoretical model, then constructing a city ground bus operation monitoring key index determination model based on characteristic engineering, taking calculated values of various indexes as model input, and taking steady-state passenger flow of a bus interval as output.
Further, in the step S1, the factors affecting the travel selection of the bus passengers include: environmental conditions, individual characteristics of travelers, travel characteristics, travel psychology and traffic service characteristics.
Further, in the step S3, data processing and calculation analysis are performed on the index directly calculated by the objective data, including the steps of: the data preprocessing and analysis of objective resident trip data and bus operation data comprise the following steps: importing a database, cleaning data, integrating multi-source data, matching and calculating the data, reducing the data and calculating indexes, and arranging the data into a model input format.
Further, in the step S3, a data set processing and a computational analysis are performed on subjective indexes which are difficult to quantify with objective data, including the steps of: collecting and analyzing index language values by utilizing questionnaire data; the language values are then quantized using a cloud model.
Further, the collection and analysis of index language values by utilizing questionnaire data comprises questionnaire design, questionnaire distribution and recovery, questionnaire data entry and cleaning and integration with objective data.
Further, depending on resident trip RP survey and SP survey data, they are input into a cloud model that processes uncertainty information to determine quantized values of the respective indexes.
Further, the quantizing the language value using the cloud model includes: the comment value numerical value interval is mapped, comment value expectations are calculated, and index value expectations are calculated.
Further, in the step S4, the building of the cumulative foreground theoretical model includes the following steps: and inputting an objective calculated value of the index, and outputting the subjective value of the passenger on the index.
Further, (1) determining an indicator;
(2) Selecting a reference point to calculate profit or loss;
(3) Calculating a value of the value;
(4) Calculating a subjective frequency weight value;
(5) And calculating the subjective value of the index.
Further, in the step S4, the building of the urban ground bus operation monitoring key index determination model based on the feature engineering includes the following steps: determining key indexes based on a parcel method in a feature selection project, searching a feature subset by using a sequence backward selection algorithm, selecting a prediction error of a generalized regression neural network as a subset evaluation criterion, and constructing a city ground bus operation monitoring key index determination model
According to the urban ground bus operation monitoring key index screening method based on passenger travel, the urban ground bus operation monitoring key index screening method based on passenger travel is designed to be combined with a multi-level travel scene in reality, and from the aspect of bus passenger travel selection, the urban ground bus operation monitoring key index with important emphasis is scientifically determined by combining subjective perception data of passengers and objective bus operation data, so that the problems of low efficiency and poor effect of the existing urban ground bus operation monitoring are solved.
The invention determines key indexes of urban ground bus running monitoring in multi-level and multi-dimension under different scenes, adds new content for bus running service monitoring research under the current urban ground bus running monitoring, provides a new angle, and the determination of the key indexes has dynamic property and expandability and can be effectively applied in practice. On the other hand, the determination of the key indexes avoids the defects of macroscopic and general aspects of the traditional index system, has prominent key points and has more guiding and reference significance. And finally, combining subjective data of passengers and objective data of bus operation to research key indexes of urban ground bus operation, so that the defect of unilateral research in the prior art 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 running condition of the urban ground buses.
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 foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of a method for screening urban ground bus operation monitoring key indexes based on passenger travel according to an embodiment of the invention;
FIG. 2 is a schematic diagram of factors affecting bus passenger travel selection in accordance with an embodiment of the present invention;
FIG. 3 is a flow chart of training and test modeling of a generalized regression neural network model according to an embodiment of the present invention;
FIG. 4 is a technical roadmap of a multi-model integrated prediction algorithm according to an embodiment of the invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
As shown in fig. 1 and fig. 4, the method for screening urban ground bus operation monitoring key indexes in passenger travel according to the embodiment of the invention comprises the following steps:
step S1, analyzing the composition and initial index of the urban ground bus operation monitoring system, comprising the following steps: analyzing the constitution and the main body of the urban ground bus operation monitoring system, and laying a foundation for subsequent content analysis. And then, researching trip decision behaviors of the travelers through documents, analyzing factors influencing the trip selection of bus passengers, and forming an initial index set determined by urban ground bus operation monitoring key indexes.
In an embodiment of the present invention, factors affecting travel selection of a bus passenger include: environmental conditions, individual characteristics of travelers, travel characteristics, travel psychology and traffic service characteristics.
Specifically, the study object of the invention is the travel selection behavior of the bus passengers, and the travel selection behavior of urban residents in all traffic modes is not considered, and three aspects of urban economic culture development conditions, traffic policies, roads and infrastructures are considered, so that the selection difference of passengers among bus travel lines is not caused, and for the travel selection of the bus passengers, only the influence factors of five aspects of environmental conditions, individual 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 for urban ground bus operation monitoring, wherein the initial index set comprises two types of indexes: (1) an index directly computable with 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 indexes in the step S2 to respectively obtain an objective index analysis result and a subjective index analysis result.
In the step, objective resident trip data and bus operation data are processed and analyzed firstly, wherein the steps comprise data importing database, data cleaning, multi-source data integration, data reduction and index calculation, and the data are arranged into a model input format. And secondly, collecting and analyzing questionnaire data, including questionnaire design, questionnaire distribution and recovery, questionnaire data input and cleaning and integration with objective data.
(1) The method for processing data and performing calculation analysis on the index directly calculated by using the objective data comprises the following steps: the data preprocessing and analysis of objective resident trip data and bus operation data comprise the following steps: importing a database, cleaning data, integrating multi-source data, matching and calculating the data, reducing the data and calculating indexes, and arranging the data into a model input format.
The first class of indexes are calculated and analyzed by means of resident trip data and bus operation data, the indexes are defined, and the calculation mode of each index is analyzed and determined. The bus IC card data, the bus real-time arrival data, the bus line basic data and the bus line station data are specifically included. The summary of the data content and the data source are shown in table 1.
TABLE 1 overview of bus data content and data Source
(2) The method for carrying out data set processing and calculation analysis on subjective indexes which are difficult to quantify by objective data comprises the following steps: collecting and analyzing index language values by utilizing questionnaire data; the language values are then quantized using a cloud model.
Index language values are collected and analyzed by utilizing questionnaire data, and the index language values comprise questionnaire design, questionnaire distribution and recovery, questionnaire data input and cleaning and integration with objective data. Wherein, the quantitative value of each index is determined by means of resident trip RP survey and SP survey data which are input into a cloud model for processing uncertainty information.
Quantifying the linguistic values using the cloud model, comprising: the comment value numerical value interval is mapped, comment value expectations are calculated, and index value expectations are calculated.
Quantification of the second category of indicators relies on resident trip RP (Revealed Preference) survey and SP (Stated Preference) survey data, which are input into a cloud model that processes uncertainty information to determine quantified values for each indicator. The second class of data specifically comprises subjective perception scores of all aspects of indexes of the bus line taken by the bus passengers. The investigator should evaluate 7 qualitative indexes and select the satisfaction degree of the investigator. The survey index is shown in table 2, and includes three latent variables and seven observed variables.
And then, data processing and analysis are carried out, namely objective resident trip data and bus operation data are processed and analyzed firstly, wherein the data processing and analysis comprises data importing database, data cleaning, multi-source data integration, data reduction and index calculation, and the data are arranged into a model input format. And secondly, collecting and analyzing questionnaire data, including questionnaire design, questionnaire distribution and recovery, questionnaire data input and cleaning and integration with objective data.
Table 2 SP subjective perception survey index of survey
And S4, integrating data in a chassis of the objective index analysis result and the subjective index analysis result, constructing an accumulated prospect theoretical model, then constructing a city ground bus operation monitoring key index determination model based on characteristic engineering, taking calculated values of various indexes as model input, and taking steady-state passenger flow of a bus interval as output.
In this step, a qualitative index quantitative model based on survey data is constructed. The passenger riding subjective perception data obtained according to SP and RP surveys exist in a qualitative form of language concepts, and the traveling experience of passengers and subjective evaluation on a ground bus running system are intuitively depicted. However, since such qualitative indexes are difficult to be directly input as a key index model, it is necessary to quantify them, introduce a cloud model, and use the index value after the quantification treatment as a part of the key index model input.
In this step, a model construction is calculated based on objective index foreground values of resident trip data. Considering that passengers are influenced by psychological factors during travel selection, an accumulated prospect theory is introduced, objective calculation values of indexes are input into the accumulated prospect theory model, and subjective values of the passengers on the indexes are output, so that the passengers are closer to travel selection scenes in the real world, and the relationship between bus service characteristic indexes and steady-state passenger flows is more reliably described.
Specifically, the building of the cumulative prospect theoretical model comprises the following steps: and inputting an objective calculated 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 profit or loss;
(3) Calculating a value of the value;
(4) Calculating a subjective frequency weight value;
(5) And calculating the subjective value of the index.
And finally, constructing a key index determining model for urban ground bus operation monitoring based on feature engineering, determining key indexes based on a parcel type method in feature selection engineering, searching a feature subset by using a sequence backward selection algorithm, selecting a prediction error of a generalized regression neural network as a subset evaluation criterion, constructing the key index determining model for urban ground bus operation monitoring, inputting calculated values of various indexes as a model, and outputting steady-state passenger flow in a bus section.
Specifically, a city ground bus operation monitoring key index determination model based on characteristic engineering is constructed, and the method comprises the following steps: and determining key indexes based on a parcel type method in the feature selection engineering, searching a feature subset by using a sequence backward selection algorithm, and constructing a city ground bus operation monitoring key index determination model by using a prediction error of a generalized regression neural network as a subset evaluation criterion.
The qualitative index and the quantitative index are respectively processed through the steps, and the processed indexes are the initial index set for monitoring the urban ground bus operation. In order to determine the key index of urban ground bus operation monitoring, a key index determination model is constructed, and the key index determination model of the backward-generalized regression neural network of the processed initial index input sequence is used for obtaining a key index determination result. FIG. 4 is a flow chart of training and test modeling of a generalized regression neural network model.
As shown in fig. 4, the training and test 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, if so, executing the step (7); otherwise, returning to the step (4);
(7) Saving the training result;
(8) Inputting a test sample, and performing model prediction;
(9) And obtaining a prediction error.
In summary, the urban ground bus operation monitoring key index screening method based on passenger travel realizes the following technical scheme and technical effects:
1. The passenger riding subjective perception data obtained according to SP and RP surveys exist in a qualitative form of language concepts, and the traveling experience of passengers and subjective evaluation on a ground bus running system are intuitively depicted. And a cloud model is introduced, and the index value after quantification is used as a part of the input of the key index model, so that the problem that the qualitative index is difficult to be directly used as the input of the key index model is solved.
2. In the actual passenger travel selection, the passenger can be influenced by subjective psychological perception, and finally the travel selection is promoted under the combined action of the objective index value and the subjective perception of the passenger. Therefore, when the influence of the quantitative indexes on the travel selection of the passengers is examined, the risk attitude preference of the passengers on the indexes is added on the basis of objective values, and the two aspects are combined to describe the value of the influence factor for the travel selection of the passengers. Therefore, the accumulated prospect theory is introduced into further calculation of objective index values, the foreground value of each index is used as a key index of the index to determine the input value of the model, so that the travel selection behavior of the passengers is more truly depicted, and the determination result of the key index is more accurate and reliable.
3. The method comprises the steps of constructing a city ground bus operation monitoring key index determining model based on characteristic engineering, determining key indexes based on a parcel type method in characteristic selection engineering, searching a characteristic subset by using a sequence backward selection algorithm, selecting a prediction error of a generalized regression neural network as a subset evaluation criterion, constructing the city ground bus operation monitoring key index determining model, inputting calculated values of various indexes as a model, and outputting steady-state passenger flow of a bus interval.
According to the urban ground bus operation monitoring key index screening method based on passenger travel, the urban ground bus operation monitoring key index screening method based on passenger travel is designed to be combined with a multi-level travel scene in reality, and from the aspect of bus passenger travel selection, the urban ground bus operation monitoring key index with important emphasis is scientifically determined by combining subjective perception data of passengers and objective bus operation data, so that the problems of low efficiency and poor effect of the existing urban ground bus operation monitoring are solved.
The invention determines key indexes of urban ground bus running monitoring in multi-level and multi-dimension under different scenes, adds new content for bus running service monitoring research under the current urban ground bus running monitoring, provides a new angle, and the determination of the key indexes has dynamic property and expandability and can be effectively applied in practice. On the other hand, the determination of the key indexes avoids the defects of macroscopic and general aspects of the traditional index system, has prominent key points and has more guiding and reference significance. And finally, combining subjective data of passengers and objective data of bus operation to research key indexes of urban ground bus operation, so that the defect of unilateral research in the prior art 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 running condition of the urban ground buses.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means 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 present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. 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 will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives, and variations may be made in the above embodiments by those skilled in the art without departing from the spirit and principles of the invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A city ground bus operation monitoring key index screening method based on passenger travel is characterized by comprising the following steps:
step S1, analyzing the composition and initial index of the urban ground bus operation monitoring system, comprising the following steps: analyzing the constitution and the main body of the urban ground bus operation monitoring system, analyzing factors influencing the travel 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 for urban ground bus operation monitoring, wherein the initial index set comprises two types of indexes: (1) an index directly computable with 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 indexes in the step S2 to respectively obtain an objective index analysis result and a subjective index analysis result;
in the step S3, the data set processing and the calculation analysis are performed on the subjective index which is difficult to quantify by the objective data, including the following steps: collecting and analyzing index language values by utilizing questionnaire data; then quantifying the language value by using the cloud model;
Determining the quantized value of each index by inputting resident trip RP survey and SP survey data into a cloud model for processing uncertainty information;
Step S4, data integration is carried out on the objective index analysis result and the subjective index analysis result, an accumulated prospect theoretical model is built, then a city ground bus operation monitoring key index determination model based on characteristic engineering is built, calculated values of all indexes are used as model input, and steady-state passenger flow in a bus interval is used as output;
In the step S1, the factors affecting the travel selection of the bus passengers include: environmental conditions, individual characteristics of travelers, travel characteristics, travel psychology and traffic service characteristics;
In the step S3, data processing and calculation analysis are performed on the index directly calculated by using the objective data, including the following steps: the data preprocessing and analysis of objective resident trip data and bus operation data comprise the following steps: importing a database, cleaning data, integrating multi-source data, matching and calculating the data, reducing the data and calculating indexes, and arranging the data into a format input by a model;
The method comprises the steps of collecting and analyzing index language values by utilizing questionnaire data, wherein the index language values comprise questionnaire design, questionnaire distribution and recovery, questionnaire data input and cleaning and integration with objective data;
The quantizing the language value by using the cloud model comprises the following steps: the comment value numerical value interval is mapped, comment value expectations are calculated, and index value expectations are calculated.
2. The method for screening urban ground bus operation monitoring key indexes based on passenger traveling according to claim 1, wherein in the step S4, the building of the accumulated prospect theoretical model comprises the following steps: and inputting an objective calculated value of the index, and outputting the subjective value of the passenger on the index.
3. The urban ground bus operation monitoring key index screening method based on passenger travel according to claim 2, wherein,
(1) Determining an index;
(2) Selecting a reference point to calculate profit or loss;
(3) Calculating a value of the value;
(4) Calculating a subjective frequency weight value;
(5) And calculating the subjective value of the index.
4. The method for screening urban ground bus operation monitoring key indexes based on passenger traveling according to claim 1, wherein in the step S4, the step of constructing the urban ground bus operation monitoring key index determination model based on feature engineering comprises the following steps: and determining key indexes based on a parcel type method in the feature selection engineering, searching a feature subset by using a sequence backward selection algorithm, and constructing a city ground bus operation monitoring key index determination model by using a prediction error of a generalized regression neural network as a subset evaluation criterion.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134105A (en) * | 2014-08-18 | 2014-11-05 | 东南大学 | Public-transit-network layout optimization method |
| CN106651181A (en) * | 2016-12-25 | 2017-05-10 | 北京工业大学 | Bus passenger flow congestion risk evaluation method under network operation condition |
| WO2018023331A1 (en) * | 2016-08-01 | 2018-02-08 | 中国科学院深圳先进技术研究院 | System and method for real-time evaluation of service index of regular public buses |
| CN107845260A (en) * | 2017-10-26 | 2018-03-27 | 杭州东信北邮信息技术有限公司 | A kind of recognition methods of user's bus trip mode |
| CN108961804A (en) * | 2018-06-20 | 2018-12-07 | 北京市交通运行监测调度中心 | Method is determined based on the alternative set of public bus network adjustment of multi objective classification intersection |
| CN109543934A (en) * | 2018-10-08 | 2019-03-29 | 北京交通大学 | The evaluation method of the overall target of urban public traffic network |
-
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134105A (en) * | 2014-08-18 | 2014-11-05 | 东南大学 | Public-transit-network layout optimization method |
| WO2018023331A1 (en) * | 2016-08-01 | 2018-02-08 | 中国科学院深圳先进技术研究院 | System and method for real-time evaluation of service index of regular public buses |
| CN106651181A (en) * | 2016-12-25 | 2017-05-10 | 北京工业大学 | Bus passenger flow congestion risk evaluation method under network operation condition |
| CN107845260A (en) * | 2017-10-26 | 2018-03-27 | 杭州东信北邮信息技术有限公司 | A kind of recognition methods of user's bus trip mode |
| CN108961804A (en) * | 2018-06-20 | 2018-12-07 | 北京市交通运行监测调度中心 | Method is determined based on the alternative set of public bus network adjustment of multi objective classification intersection |
| CN109543934A (en) * | 2018-10-08 | 2019-03-29 | 北京交通大学 | The evaluation method of the overall target of urban public traffic network |
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