CN113409571B - Judging method and device for setting bus lane, storage medium and terminal - Google Patents
Judging method and device for setting bus lane, storage medium and terminal Download PDFInfo
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Abstract
The invention discloses a judging method for setting a bus lane, which comprises the following steps: acquiring various factors influencing the setting of a bus lane to construct a hierarchical structure model; the hierarchical structure model comprises a target layer, a first factor layer and a second factor layer; calculating a weighted value of the first factor layer aiming at the target layer to generate a first weighted value; calculating a weighted value of the second factor layer aiming at each membership factor of the first factor layer to generate a plurality of second weighted values; calculating and generating a weighted value based on a hierarchical analysis algorithm; when the consistency ratio of the judgment matrixes of the first factor layer and the plurality of second factor layers is smaller than a preset threshold value, performing combined weight vector calculation according to a first weight value and a plurality of second weight values, and then generating a target weight value of the second factor layer for the target layer; acquiring and preprocessing various types of data of a target road in a preset time period; and multiplying the matrix generated after the preprocessing by the target weight value to generate a comprehensive evaluation value, and judging whether the target road is provided with a bus lane in a preset time period.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to a method and a device for judging whether a bus lane is set, a storage medium and a terminal.
Background
In the past decades, buses have once become the largest service carrier in the urban public travel service field, and bear the travel demands of mass public. In recent years, with the development of cities, rail transit is increasingly developed, and in addition, the holding amount of private cars is sharply increased, road congestion is increasingly serious, so that the overall service requirement and capacity of buses are gradually reduced, and therefore how to improve the energy efficiency of the bus service and improve the occupancy rate of public green trips is a very serious problem.
In the prior art, the planning of a bus lane is researched mainly on the basis of factors such as bus lines and bus positioning, and the bus running efficiency is improved. However, on the whole, the research directions are more comprehensive, and the research is not carried out from the overall view of bus running, so that the public traffic lane planning has more defects, for example, the planned public traffic lane is a fixed traffic lane, and the public traffic lane cannot be dynamically adjusted according to different time periods, so that the public traffic service energy efficiency is reduced, and the occupancy rate of public green trips is reduced.
Disclosure of Invention
The embodiment of the application provides a method and a device for judging the setting of a bus lane, a storage medium and a terminal. 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 application provides a method for determining setting of a bus lane, where the method includes:
obtaining multiple types of factors influencing the setting of a bus lane, and constructing a hierarchical structure model based on the multiple types of factors; the hierarchical structure model at least comprises a target layer, a first factor layer and a second factor layer;
calculating a weighted value of the first factor layer aiming at the target layer to generate a first weighted value;
calculating a weight value of each membership factor of the first factor layer by the second factor layer to generate a plurality of second weight values; wherein, the weighted value is calculated and generated based on a hierarchical analysis algorithm;
calculating consistency ratios of the judgment matrixes of the first factor layer and the plurality of second factor layers, and when the consistency ratios of the judgment matrixes of the first factor layer and the plurality of second factor layers are smaller than a preset threshold, performing combined weight vector calculation according to a first weight value and a plurality of second weight values to generate a target weight value of the second factor layer for a target layer;
acquiring and preprocessing various types of data of a target road in a preset time period; the multi-class data comprises data corresponding to each class of factor in the multi-class factors;
taking the product of the matrix generated after the preprocessing and the target weight value to generate a comprehensive evaluation value;
and judging whether the target road is provided with a bus lane within a preset time period or not according to the comprehensive evaluation value.
Optionally, determining whether the target road is set with a bus lane in a preset time period according to the comprehensive evaluation value includes:
loading a judgment grade table;
identifying a value range to which the comprehensive evaluation value belongs from the judgment grade table;
when the corresponding evaluation index of the threshold value range in the judgment grade table is a suggestion or recommendation, determining that the target road can be provided with a bus lane within a preset time period;
outputting information that a target road can set a bus lane in a preset time period and sending the information to a relevant department;
alternatively, the first and second electrodes may be,
when the corresponding evaluation index of the threshold value range in the judgment grade table is not suggested or recommended, determining that the target road cannot be provided with the bus lane within a preset time period;
and outputting information that the target road cannot set the bus lane in a preset time period to relevant departments.
Optionally, calculating a weight value of the first factor layer for the target layer to generate a first weight value includes:
generating a first questionnaire according to the target layer and the first factor layer, and displaying the first questionnaire;
when a selection instruction input aiming at the answer options of each question in the first questionnaire is received, obtaining a first selected answer aiming at each question in the first questionnaire;
generating a first judgment matrix based on the first selected answer;
normalizing each row of parameters in the first judgment matrix according to rows;
and summing the matrixes normalized by columns according to rows and then performing normalization processing to generate a first weight value.
Optionally, calculating a weight value of the second factor layer for the first factor layer, calculating a weight value of the second factor layer for each subordinate factor of the first factor layer, and generating a plurality of second weight values, includes:
generating a second questionnaire according to the first factor layer and the second factor layer, and displaying the second questionnaire;
when a selection instruction input aiming at the answer options of each question in the second questionnaire is received, obtaining a second selected answer aiming at each question in the second questionnaire;
generating a plurality of second decision matrices based on the second selected answer;
normalizing each column parameter of each of the plurality of second decision matrices by column;
and summing the matrixes normalized by columns by rows and then performing normalization processing to generate a plurality of second weight values.
Optionally, calculating a consistency ratio of the determination matrices of the first factor layer and the plurality of second factor layers includes:
calculating a first maximum characteristic root according to the first judgment matrix;
calculating a first consistency index according to the first maximum characteristic root;
inquiring a first random consistency check value corresponding to the parameter number of the first judgment matrix from a preset random consistency check table;
determining the ratio of the first consistency index to the first random consistency check value as the consistency ratio corresponding to the first weight value;
and the number of the first and second groups,
calculating a plurality of second maximum feature roots according to the plurality of second judgment matrixes;
calculating a plurality of second consistency indexes according to the plurality of second maximum characteristic roots;
inquiring a plurality of second random consistency check values corresponding to the parameter quantity of a plurality of second judgment matrixes from a preset random consistency check table;
and determining the ratio of the plurality of second consistency indexes to the plurality of second random consistency check values as consistency ratios corresponding to the plurality of second weight values.
Optionally, acquiring and preprocessing multiple types of data of the target road in a preset time period; wherein the multi-class data includes data corresponding to each of the multi-class factors, and includes:
acquiring various types of data of a target road in a preset time period;
and carrying out standardization and forward processing on the multi-class data to generate a matrix.
Optionally, the method further includes:
periodically acquiring a data set of relevant factors influencing the setting of a bus lane of a target road section;
preprocessing the data set of each period and then generating a matrix corresponding to each period;
after the matrix corresponding to each period is multiplied by the target weight value, generating a comprehensive evaluation value of each period;
and dynamically setting a bus lane based on the comprehensive evaluation value of each period.
In a second aspect, an embodiment of the present application provides a determination device for setting a bus lane, where the device includes:
the model building module is used for obtaining various factors influencing the setting of the bus lane and building a hierarchical structure model based on the various factors; the hierarchical structure model at least comprises a target layer, a first factor layer and a second factor layer;
the first weight value calculating module is used for calculating a weight value of the first factor layer aiming at the target layer to generate a first weight value;
the plurality of second weight value calculation modules are used for calculating the weight values of the second factor layer aiming at the first factor layer, calculating the weight values of the second factor layer aiming at each subordinate factor of the first factor layer and generating a plurality of second weight values; wherein, the weighted value is calculated and generated based on a hierarchical analysis algorithm;
the target weight value generation module is used for calculating the consistency ratio of the judgment matrixes of the first factor layer and the second factor layers, and when the consistency ratio of the judgment matrixes of the first factor layer and the second factor layers is smaller than a preset threshold value, performing combined weight vector calculation according to the first weight value and the second weight values to generate a target weight value of the second factor layer for the target layer;
the data acquisition module is used for acquiring and preprocessing various types of data of the target road in a preset time period; the multi-class data comprises data corresponding to each class of factor in the multi-class factors;
a comprehensive evaluation value generation module, configured to generate a comprehensive evaluation value by multiplying the preprocessed matrix by the target weight value;
and the judging module is used for judging whether the target road is provided with a bus lane in a preset time period according to the comprehensive evaluation value.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, the judging device for setting the bus lane firstly acquires various factors influencing the setting of the bus lane and constructs a hierarchical structure model based on the various factors; the hierarchical structure model at least comprises a target layer, a first factor layer and a second factor layer, the first factor layer is calculated to generate a first weight value aiming at the weight value of the target layer, the second factor layer is calculated to generate a plurality of second weight values aiming at the weight value of each factor of the first factor layer; calculating and generating a weighted value based on a hierarchical analysis algorithm, then calculating the consistency ratio of the judgment matrixes of the first factor layer and the plurality of second factor layers, when the consistency ratio of the judgment matrixes of the first factor layer and the plurality of second factor layers is smaller than a preset threshold value, performing combined weight vector calculation according to the first weighted value and the plurality of second weighted values to generate a target weighted value of the second factor layer for a target layer, and then acquiring and preprocessing multi-class data of the target road in a preset time period; and finally, judging whether a bus lane is set on a target road within a preset time period according to the comprehensive evaluation value. According to the method and the device, the model is established by adopting various factors of the bus lane, and the model is analyzed and calculated by adopting a hierarchical analysis algorithm, so that the reasonability of bus lane construction is improved, and the invalid occupation of road traffic resources is further reduced.
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 of a determination method for setting a bus lane according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a hierarchical structure model constructed according to multiple factors according to an embodiment of the present application;
FIG. 3 is a scale table for constructing a judgment matrix according to an embodiment of the present disclosure;
fig. 4 is a schematic device diagram of a determination device for setting a bus lane according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a terminal according to an embodiment of the present application.
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 apparatus 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 application provides a method and a device for judging whether a bus lane is set, a storage medium and a terminal, which are used for solving the problems in the related technical problems. According to the technical scheme, the model is established by adopting multiple types of factors of the bus lane, and the model is analyzed and calculated by adopting a hierarchical analysis algorithm, so that the reasonability of bus lane construction is improved, the invalid occupation of road traffic resources is further reduced, and the detailed description is given by adopting an exemplary embodiment.
The method for determining a bus lane according to the embodiment of the present application will be described in detail below with reference to fig. 1 to 3. The method may be implemented by means of a computer program, and may be executed on a determination device for setting a bus lane based on von neumann system. The computer program may be integrated into the application or may run as a separate tool-like application. The device for determining the bus lane in the embodiment of the present application may be a user terminal, including but not limited to: personal computers, tablet computers, handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to a wireless modem, and the like. The user terminals may be called different names in different networks, for example: user equipment, access terminal, subscriber unit, subscriber station, mobile station, remote terminal, mobile device, user terminal, wireless communication device, user agent or user equipment, cellular telephone, cordless telephone, Personal Digital Assistant (PDA), terminal equipment in a 5G network or future evolution network, and the like.
Referring to fig. 1, a schematic flow chart of a method for determining setting of a bus lane is provided in an embodiment of the present application. As shown in fig. 1, the method of the embodiment of the present application may include the following steps:
s101, obtaining multiple types of factors influencing the setting of a bus lane, and constructing a hierarchical structure model based on the multiple types of factors; the hierarchical structure model at least comprises a target layer, a first factor layer and a second factor layer;
the first factor layer comprises road composition, current bus lane situation, traffic condition, bus operation and bus station state. The second factor layer comprises the number of lanes, the type of the bus station, the current situation of the bus lane, the illegal condition of occupying the bus lane, the average traffic speed, the traffic flow, the bus running speed, the saturation in the bus, the parking time of the bus, the number of passengers getting on the bus station, the number of passengers getting off the bus station and the number of passengers waiting at the bus station.
Generally, the multiple types of factors include road and lane data, road and bus station data, bus line data, road average speed data, road traffic flow data, illegal data of occupying bus lanes, road section driving bus saturation data, bus station on-off data, and bus station waiting people number data.
For example, the road and lane data refers to the number of one-way lanes of a road section, generally 1 lane, 2 lanes, 3 lanes and 4 lanes, and is the basis for setting a bus lane; the current bus lane situation refers to whether a bus lane is set in a one-way lane of a road section, and the evaluation result of the method is directly influenced; the road bus stop data refers to the bus stop type in a road section one-way direction, and is generally large, medium and small according to the length; the average speed data represents the passing condition of the road section, namely the traffic flow commonly used in the traffic management industry, and reflects the running condition of the bus in the road section; the traffic flow data refers to the running number of motor vehicles on a road section, the traffic pressure of a reaction road section and great influence on the arrangement of a bus lane; the illegal data of the occupied bus lane refers to the illegal quantity of the occupied bus lane generated in one direction of the road section, and whether the rest of the illegal data have a strong relation with the existing bus lane or not; the number of the bus lines refers to the number of the bus lines carried by the road section, and reflects the importance of bus driving in the road section; the in-bus saturation refers to the full-load condition in the bus running in the road section, and reflects the passing requirement of the bus; the data of the passenger flow on or off the bus station refers to the passenger flow on or off the bus station as a whole; the data of the number of waiting people at the bus station refers to the number of people waiting at the bus station; the bus running speed refers to the bus speed running in a road section and has a strong relation with whether a bus lane exists or not and the average speed of a road; the bus parking time refers to the parking time of the bus at the bus station.
It should be noted that the time data of the date level and the hour level is only used as an analysis dimension, and does not participate in the comprehensive evaluation calculation.
Generally, after acquiring multiple types of factors for setting a bus lane, a hierarchical structure model may be constructed according to the acquired multiple types of factors, where the constructed hierarchical structure model includes at least a target layer, a first factor layer, and a second factor layer, as shown in fig. 2.
In a possible implementation manner, when determining whether a road is set as a bus lane, firstly, multiple types of factors for setting the bus lane are obtained, then, operation instructions for the multiple types of factors are received, the multiple types of factors are combined according to the operation instructions to generate a hierarchical structure model, and the constructed structure model comprises a target layer, a first factor layer and a second factor layer, wherein the target layer is in associated mapping with the first factor layer, and the first factor layer is in associated mapping with the second factor layer. The factor set of the first factor layer can be expressed as U ═ road composition, traffic condition, bus operation, bus station state, bus lane status }, and the second factor layer belongs to the first factor layer. For example, the number of lanes and the type of bus stop in the second factor layer belong to the road composition in the first factor layer, the current bus lane situation and the illegal situation of occupying the bus lane in the second factor layer belong to the current bus lane situation in the first factor layer, the average traffic speed and the traffic flow in the second factor layer belong to the traffic situation in the first factor layer, the bus running speed and the internal saturation of the bus in the second factor layer belong to the bus running in the first factor layer, and the bus stop duration, the number of passengers getting on the bus stop, the number of passengers getting off the bus stop and the number of passengers waiting at the bus stop in the second factor layer belong to the bus stop state in the first factor layer.
S102, calculating a weight value of a first factor layer aiming at a target layer to generate a first weight value;
generally, when calculating a weight value of a first factor layer for a target layer, a determination matrix needs to be generated, when generating the determination matrix, a user terminal needs to generate a plurality of questionnaire selection questions according to a relationship between the first factor layer and the target layer, a user selects options in the selection questions according to a preset scale table, and after selection, the terminal calculates scores according to answers selected by the user to form the determination matrix, where the preset scale table is shown in fig. 3, for example.
In the embodiment of the application, when a weight value of a first factor layer for a target layer is calculated, a first questionnaire is generated according to the target layer and the first factor layer, the first questionnaire is displayed, when a selection instruction input for answer options of each topic in the first questionnaire is received, a first selected answer for each topic in the first questionnaire is obtained, a first judgment matrix is generated based on the first selected answer, the first judgment matrix is normalized by a sum-product method in a column manner to obtain a first weight value, specifically, each column parameter in the first judgment matrix can be normalized by a column, and finally, the matrix normalized by the column is summed by a row and normalized to generate the first weight value.
The step of summing the matrixes normalized by columns according to rows and performing normalization processing means that after summing the matrixes normalized by columns according to rows, each parameter in the obtained column matrix is divided by the number of the influencing factors so as to perform normalization processing, and a first weight value is obtained.
In a possible implementation manner, a user selects options in the choice questions according to a preset scale table, and after selection, the terminal calculates scores according to answers selected by the user to form a first judgment matrix as follows:
after the first judgment matrix is obtained, performing column-by-column normalization according to the first judgment matrix and a sum-product method to obtain a first weight value:
note that the first weight value is a matrix.
S103, calculating the weight value of the second factor layer aiming at the first factor layer, calculating the weight value of the second factor layer aiming at each subordinate factor of the first factor layer, and generating a plurality of second weight values;
and the weighted values are calculated and generated based on a hierarchical analysis algorithm.
Generally, a hierarchical analysis algorithm, abbreviated as AHP, refers to a decision-making method for performing qualitative and quantitative analysis based on the decomposition of elements always related to decision-making into a hierarchy of targets, criteria, schemes, and the like.
In a possible implementation manner, when calculating a weight value of a second factor layer for each factor of a first factor layer, first generating a second questionnaire according to the first factor layer and the second factor layer, displaying the second questionnaire, then when receiving a selection instruction input for an answer option of each question in the second questionnaire, obtaining a second selected answer for each question in the second questionnaire, then generating a plurality of second decision matrices based on the second selected answer, performing column-wise normalization on each column parameter of each plurality of second decision matrices in the plurality of second decision matrices, and finally performing row-wise summation and then normalization on the matrices after column-wise normalization to generate a plurality of second weight values, where it is required to say that the second weight values are also matrices.
It should be noted that after summing the matrix normalized by columns by rows, each parameter in the obtained column matrix is divided by the number of the influencing factors to perform normalization processing, so as to obtain a second weight value.
S104, calculating consistency ratios of the judgment matrixes of the first factor layer and the second factor layers, and when the consistency ratios of the judgment matrixes of the first factor layer and the second factor layers are smaller than a preset threshold value, performing combined weight vector calculation according to a first weight value and a plurality of second weight values to generate a target weight value of the second factor layer for a target layer;
in the embodiment of the application, when the consistency ratio of the first weight value is calculated, a first maximum feature root is calculated according to a first judgment matrix, a first consistency index is calculated according to the first maximum feature root, a first random consistency check value corresponding to the number of parameters of the first judgment matrix is inquired from a preset random consistency check table, and finally the ratio of the first consistency index to the first random consistency check value is determined as the consistency ratio corresponding to the first weight value.
For example, when the first determination matrix is
In the embodiment of the present application, when the consistency ratios of the second weighted values are calculated, first, the second maximum feature roots are calculated according to the second decision matrices, then, the second consistency indexes are calculated according to the second maximum feature roots, then, the second random consistency check values corresponding to the number of parameters of the second decision matrices are queried from the preset random consistency check table, and finally, the ratio of the second consistency indexes to the second random consistency check values is determined as the consistency ratio corresponding to the second weighted values.
Further, when the consistency ratio of the judgment matrixes of the first factor layer and the plurality of second factor layers is smaller than a preset threshold, a target weight value of the second factor layer for the target layer may be generated after performing combined weight vector calculation according to the first weight value and the plurality of second weight values.
In a possible implementation manner, after the combined weight vector is calculated according to the first weight value and the plurality of second weight values, the target weight value of the second factor layer for the target layer (bus lane setting evaluation) is obtained as follows:
that is, the above-mentioned target weight values are, in order, the number of factor lanes in the second factor layer, the type of bus stop, the current state of bus lane, the setting of bus lane, the average speed of traffic, the flow of traffic, the travel speed of bus, the saturation in bus, the length of time of bus stop, the number of persons getting on bus station, the number of persons getting off bus station, the influence weight of the number of persons waiting at bus station set for the bus lane of the target layer, specifically, 0.0181 is the weight value set for bus lane for the number of lanes, 0.0543 is the weight value set for bus lane for the type of bus stop, 0.0579 is the weight value set for bus lane for the current state of bus lane, 0.0145 is the weight value set for bus lane for the violation of occupying bus lane, 0.0471 is the weight value for road, 0.0941 is the weight value set for bus lane for the flow of traffic, and 0.0692 is the weight value set for bus lane for bus travel speed, 0.0241 is the weight value that saturation set up to the bus lane in the bus, and 0.0256 is the weight value that the length of time was set up to the bus lane when the bus was parked, and 0.0092 is the weight value that the number of getting on the bus station set up to the bus lane, 0.4081 is the weight value that the number of getting off the bus station set up to the bus lane, and 0.0680 is the weight value that the number of waiting at the bus station set up to the bus lane.
S105, acquiring and preprocessing multiple types of data of the target road in a preset time period;
the multi-class data includes data corresponding to each of the multi-class factors, such as date type data, time period data, current bus lane status, and bus stop type included in a certain time period of the target road.
Generally, the preprocessing of data includes a normalization process and a forward process.
In a possible implementation mode, firstly, multi-class data of a target road in a preset time period are obtained, then the multi-class data are subjected to standardization processing by adopting a standard score method to generate multi-class standardized data, and finally the multi-class standardized data are subjected to forward processing to generate a matrix.
Specifically, the standard score method, also known as z-score (z-score), is the process of dividing the difference between a number and a mean by the standard deviation. In statistics, a standard score is the number of symbols for which the value of an observation or data point is higher than the standard deviation of the average of the observed or measured values.
For example, statistical data of the hour dimension of each factor of a certain road section A in working days 7:00-12:00 is obtained to form a 5X 12 matrix, and the data level difference and the direction difference are considered, so that normalization processing and uniform forward processing are performed according to z score, and a data standard set X of each factor of the road section in the time dimension is realized.
The method comprises the following specific steps: in the first step, factor data is obtained according to the contents in table 1.
TABLE 1
| Type of date | Time period | Road lane | Current situation of bus lane | Site type | …… |
| Working day | 7:00-8:00 |
And step two, carrying out standardization treatment on various types of data:
the step comprises two contents, namely, the various data are subjected to standardization processing according to z score, the calculation process is simple, and simple logic calculation based on the mean value and the standard deviation is performed; the other is that the reverse data is directly negative (i.e. x'i＝-xi) Conversion is performed using the negative of the absolute value of the mean difference (i.e., x ') for the comfort data'i＝-|xi-k |) is converted.
In one possible implementation, the result may be:
where i is 5, j is 12, and x is each parameter in the matrix.
S106, multiplying the matrix generated after the preprocessing by the target weight value to generate a comprehensive evaluation value;
in a possible implementation manner, the normalized data and the weighted values of each factor are calculated according to a comprehensive evaluation model to obtain a comprehensive evaluation value Z of an hour dimension, and a specific calculation formula is as follows:
and S107, judging whether the target road is provided with a bus lane in a preset time period according to the comprehensive evaluation value.
In a possible implementation mode, a judgment grade table is loaded, a value range to which a comprehensive evaluation value belongs is identified from the judgment grade table, when an evaluation index corresponding to a threshold value range in the judgment grade table is a suggestion or recommendation, a target road is determined to be capable of setting a bus lane in a preset time period, information that the target road is capable of setting the bus lane in the preset time period is output and sent to relevant departments to confirm, after a confirmation instruction that the relevant departments confirm that the bus lane is set in the preset time period is received, a bus lane opening instruction is started, a bus lane indicating device on the road is opened, and the vehicle is indicated to obey a bus lane relevant control system and instruction.
In another possible implementation manner, when the corresponding evaluation index of the threshold value range in the judgment grade table is not suggested or recommended, firstly, it is determined that the target road cannot set the bus lane within the preset time period, and then the output target road cannot set the bus within the preset time period
The information of the lane is sent to the relevant department.
Further, a plurality of time periods can be analyzed according to the steps S105 to S107, and the time periods are expressed according to a table mode, namely, the comprehensive evaluation value of the construction of the bus lane of the road section in the working day and the range of 7:00 to 12:00 per hour is shown in Table 2.
TABLE 2
Then, a bus lane setting suggestion evaluation set is established, wherein the set comprises 4 indexes of recommendation, suggestion, non-recommendation and the like, the evaluation level of the comfort level corresponding to the corresponding time period is searched according to the corresponding relation between the preset comprehensive evaluation value and the evaluation set, the corresponding relation between the comprehensive evaluation value (threshold value) and the evaluation level can be dynamically adjusted according to actual needs, and the specific table is shown in table 3:
TABLE 3
| Evaluation index | Is not recommended | Do not suggest | Advising | Recommending |
| Value range |
Finally, the comprehensive evaluation value in table 2 is compared with the threshold value in table 3, and the evaluation index for each time period is analyzed, for example, as shown in table 4.
TABLE 4
In a preferred embodiment, in a road section with a flexibly adjustable bus lane, an evaluation result obtained by adopting the model lane can be used as a reference/basis for flexibly adjusting the setting time of the bus lane.
Generating a corresponding execution instruction according to the evaluation result of the bus lane setting obtained by the model, recommending to set a bus lane when a certain road section is 8:00-9:00 in a working day if the calculation result shows that the bus lane is recommended, sending the corresponding instruction to a road management and control party, starting a bus lane signal instruction (such as a bus lane signal lamp) by the road management and control party, using an original non-bus lane as the bus lane management and control party, and after a period of execution, closing the bus lane signal instruction when the evaluation result of the bus lane setting obtained according to the model shows that the bus lane setting is not recommended, and returning the road to a normal lane, so that the setting of the bus lane can be dynamically adjusted according to the traffic condition of the current road section, the minimum effective setting period of the bus lane can be not limited to an hour level, but can be fine-grained to a half-hour level, and a quarter-hour level to be more accurate, flexibly adjust the setting time interval of the bus lane, improve traffic, improve road utilization rate,
it should be noted that, in order to implement dynamic setting of the bus lane, on one hand, a data set of relevant factors affecting the setting of the bus lane at a target road segment is periodically acquired, and after preprocessing, a sum product of target weight values corresponding to the factors is obtained, so as to obtain a comprehensive evaluation value, where the comprehensive evaluation value corresponds to an acquisition cycle of data, and an acquisition cycle may be 1h, or may also be 30min, or even may be 15min apart, so as to implement dynamic setting of the bus lane, and in particular, under a short-term high traffic flow or congestion condition, such as a large flow of coming-out passengers or going-back passengers in the afternoon on a holiday, or a local congestion condition, a short-term bus lane is set, so that a bus can pass quickly, and congestion is reduced by way of active road management and control.
Moreover, the bus lanes are dynamically arranged according to needs, the bus lanes can be used as common lanes in unnecessary arrangement time, the utilization efficiency of roads can be improved, and the waste of resources is avoided.
It should be noted that, when the bus lane is flexibly set, the model is set in the traffic control background, the parameters of the influencing factors in the set factors influencing the bus lane are continuously obtained and monitored in real time, the evaluation result of the bus lane setting is obtained according to the model, and the evaluation result can be further manually confirmed by a traffic control department and then sent to an execution end (such as a bus lane signal control end) of the bus lane setting so as to execute the operation instruction containing the bus lane. All road sections can be judged according to the method, and the bus lane can be established or operated in an auxiliary mode.
In the embodiment of the application, the judging device for setting the bus lane firstly acquires various factors influencing the setting of the bus lane and constructs a hierarchical structure model based on the various factors; the hierarchical structure model at least comprises a target layer, a first factor layer and a second factor layer, the first factor layer is calculated to generate a first weight value aiming at the weight value of the target layer, the second factor layer is calculated to generate a plurality of second weight values aiming at the weight value of the first factor layer, and the second factor layer is calculated to generate a plurality of second weight values aiming at the weight value of each subordinate factor of the first factor layer; calculating and generating a weighted value based on a hierarchical analysis algorithm, then calculating the consistency ratio of the judgment matrixes of the first factor layer and the plurality of second factor layers, when the consistency ratio of the judgment matrixes of the first factor layer and the plurality of second factor layers is smaller than a preset threshold value, performing combined weight vector calculation according to the first weighted value and the plurality of second weighted values to generate a target weighted value of the second factor layer for a target layer, and then acquiring and preprocessing multi-class data of the target road in a preset time period; and finally, judging whether a bus lane is set on a target road within a preset time period according to the comprehensive evaluation value. According to the method and the device, the model is established by adopting various factors of the bus lane, and the model is analyzed and calculated by adopting a hierarchical analysis algorithm, so that the reasonability of bus lane construction is improved, and the invalid occupation of road traffic resources is further reduced.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Referring to fig. 4, a schematic structural diagram of a determination device for setting a bus lane according to an exemplary embodiment of the present invention is shown. The device for determining the set bus lane can be realized by software, hardware or a combination of the software and the hardware to form all or part of the terminal. The apparatus 1 includes a model construction module 10, a first weight value calculation module 20, a plurality of second weight value calculation modules 30, a target weight value generation module 40, a data acquisition module 50, a comprehensive evaluation value generation module 60, and a determination module 70.
The model building module 10 is used for obtaining various factors influencing the setting of the bus lane and building a hierarchical structure model based on the various factors; the hierarchical structure model at least comprises a target layer, a first factor layer and a second factor layer;
a first weight value calculating module 20, configured to calculate a weight value of the first factor layer for the target layer to generate a first weight value;
a plurality of second weight value calculating modules 30, configured to calculate a weight value of the second factor layer for the first factor layer, calculate a weight value of the second factor layer for each subordinate factor of the first factor layer, and generate a plurality of second weight values; wherein, the weighted value is calculated and generated based on a hierarchical analysis algorithm;
a target weight value generation module 40, configured to calculate consistency ratios of the determination matrices of the first factor layer and the plurality of second factor layers, and when the consistency ratios of the determination matrices of the first factor layer and the plurality of second factor layers are smaller than a preset threshold, perform combined weight vector calculation according to the first weight value and the plurality of second weight values, and generate a target weight value of the second factor layer for the target layer;
the data acquisition module 50 is used for acquiring and preprocessing various types of data of the target road in a preset time period; the multi-class data comprises data corresponding to each class of factor in the multi-class factors;
a comprehensive evaluation value generation module 60, configured to multiply the matrix generated after the preprocessing and the target weight value to generate a comprehensive evaluation value;
and the judging module 70 is used for judging whether the target road is provided with a bus lane in a preset time period according to the comprehensive evaluation value.
It should be noted that, when the determining apparatus for setting a bus lane provided in the foregoing embodiment executes the determining method for setting a bus lane, the division of each function module is merely used as an example, and in practical applications, the function distribution may be completed by different function modules according to needs, that is, the internal structure of the device may be divided into different function modules, so as to complete all or part of the functions described above. In addition, the determining apparatus for setting a bus lane and the determining method for setting a bus lane provided by the embodiments belong to the same concept, and the embodiment of the method for embodying the implementation process is described in detail in the embodiments, and is not repeated herein.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the embodiment of the application, the judging device for setting the bus lane firstly acquires various factors influencing the setting of the bus lane and constructs a hierarchical structure model based on the various factors; the hierarchical structure model at least comprises a target layer, a first factor layer and a second factor layer, the first factor layer is calculated to generate a first weight value aiming at the weight value of the target layer, the second factor layer is calculated to generate a plurality of second weight values aiming at the weight value of the first factor layer, and the second factor layer is calculated to generate a plurality of second weight values aiming at the weight value of each subordinate factor of the first factor layer; calculating and generating a weighted value based on a hierarchical analysis algorithm, then calculating the consistency ratio of the judgment matrixes of the first factor layer and the plurality of second factor layers, when the consistency ratio of the judgment matrixes of the first factor layer and the plurality of second factor layers is smaller than a preset threshold value, performing combined weight vector calculation according to the first weighted value and the plurality of second weighted values to generate a target weighted value of the second factor layer for a target layer, and then acquiring and preprocessing multi-class data of the target road in a preset time period; and finally, judging whether the target road is provided with a bus lane within a preset time period according to the comprehensive evaluation value. According to the method and the device, the model is established by adopting various factors of the bus lane, and the model is analyzed and calculated by adopting a hierarchical analysis algorithm, so that the reasonability of bus lane construction is improved, and the invalid occupation of road traffic resources is further reduced.
The present invention also provides a computer readable medium having stored thereon program instructions that, when executed by a processor, implement the method for determining setting a bus lane provided by the above-described method embodiments. The present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the method of determining setting a bus lane of the above-described method embodiments.
Please refer to fig. 5, which provides a schematic structural diagram of a terminal according to an embodiment of the present application. As shown in fig. 5, terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 5, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a determination application program that sets a bus lane.
In the terminal 1000 shown in fig. 5, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 1001 may be configured to call the determination application program for setting the bus lane stored in the memory 1005, and specifically perform the following operations:
obtaining multiple types of factors influencing the setting of a bus lane, and constructing a hierarchical structure model based on the multiple types of factors; the hierarchical structure model at least comprises a target layer, a first factor layer and a second factor layer;
calculating a weighted value of the first factor layer aiming at the target layer to generate a first weighted value;
calculating the weight value of the second factor layer aiming at the first factor layer, calculating the weight value of the second factor layer aiming at each membership factor of the first factor layer, and generating a plurality of second weight values; wherein, the weighted value is calculated and generated based on a hierarchical analysis algorithm;
calculating consistency ratios of the judgment matrixes of the first factor layer and the plurality of second factor layers, and when the consistency ratios of the judgment matrixes of the first factor layer and the plurality of second factor layers are smaller than a preset threshold value, performing combined weight vector calculation according to the first weight values and the plurality of second weight values to generate a target weight value of the second factor layer for a target layer;
acquiring and preprocessing various types of data of a target road in a preset time period; the multi-class data comprises data corresponding to each class of factor in the multi-class factors;
taking the product of the matrix generated after the preprocessing and the target weight value to generate a comprehensive evaluation value;
and judging whether the target road is provided with a bus lane within a preset time period or not according to the comprehensive evaluation value.
In one embodiment, the processor 1001, when performing the determination of whether the target road sets a bus lane within a preset time period according to the comprehensive evaluation value, specifically performs the following operations:
loading a judgment grade table;
identifying a value range to which the comprehensive evaluation value belongs from the judgment grade table;
when the corresponding evaluation index of the threshold value range in the judgment grade table is a suggestion or recommendation, determining that the target road can be provided with a bus lane within a preset time period;
outputting information that a target road can set a bus lane in a preset time period and sending the information to a relevant department;
alternatively, the first and second liquid crystal display panels may be,
when the corresponding evaluation index of the threshold value range in the judgment grade table is not suggested or recommended, determining that the target road cannot be provided with the bus lane within a preset time period;
and outputting information that the target road cannot set the bus lane in a preset time period to relevant departments.
In one embodiment, when performing the calculation of the first factor layer to generate the first weight value for the weight value of the target layer, the processor 1001 specifically performs the following operations:
generating a first questionnaire according to the target layer and the first factor layer, and displaying the first questionnaire;
when a selection instruction input aiming at the answer option of each question in the first questionnaire is received, acquiring a first selected answer aiming at each question in the first questionnaire;
generating a first judgment matrix based on the first selected answer;
normalizing each column parameter in the first judgment matrix according to columns;
and summing and averaging the matrixes normalized by columns according to rows to generate a first weight value.
In one embodiment, when performing the calculation of the weight value of the second factor layer for the first factor layer and the calculation of the weight value of the second factor layer for each subordinate factor of the first factor layer to generate a plurality of second weight values, the processor 1001 specifically performs the following operations:
generating a second questionnaire according to the first factor layer and the second factor layer, and displaying the second questionnaire;
when a selection instruction input aiming at the answer options of all the questions in the second questionnaire is received, second selected answers aiming at all the questions in the second questionnaire are obtained;
generating a plurality of second decision matrices based on the second selected answer;
normalizing each column parameter of each of the plurality of second decision matrices by column;
and summing and averaging the matrixes normalized by columns according to rows to generate a plurality of second weight values.
In one embodiment, the processor 1001 specifically performs the following operations when performing the calculation of the consistency ratio of the determination matrices of the first factor layer and the plurality of second factor layers:
calculating a first maximum characteristic root according to the first judgment matrix;
calculating a first consistency index according to the first maximum characteristic root;
inquiring a first random consistency check value corresponding to the parameter quantity of the first judgment matrix from a preset random consistency check table;
determining the ratio of the first consistency index to the first random consistency check value as the consistency ratio corresponding to the first weight value;
and the number of the first and second groups,
calculating a plurality of second maximum feature roots according to the plurality of second judgment matrixes;
calculating a plurality of second consistency indexes according to the plurality of second maximum characteristic roots;
inquiring a plurality of second random consistency check values corresponding to the number of parameters of a plurality of second judgment matrixes from a preset random consistency check table;
and determining the ratio of the plurality of second consistency indexes to the plurality of second random consistency check values as consistency ratios corresponding to the plurality of second weight values.
In one embodiment, the processor 1001 is performing the acquisition and the pre-processing of multiple types of data of the target road within a preset time period; when the multi-class data includes data corresponding to each of the multi-class factors, the following operations are specifically executed:
acquiring various types of data of a target road in a preset time period;
standardizing the multiple types of data by adopting a standard score method to generate multiple types of standardized data;
and carrying out forward processing on the multiple types of standardized data to generate a matrix.
In the embodiment of the application, the judging device for setting the bus lane firstly acquires various factors influencing the setting of the bus lane and constructs a hierarchical structure model based on the various factors; the hierarchical structure model at least comprises a target layer, a first factor layer and a second factor layer, the first factor layer is calculated to generate a first weight value aiming at the weight value of the target layer, the second factor layer is calculated to generate a plurality of second weight values aiming at the weight value of the first factor layer, and the second factor layer is calculated to generate a plurality of second weight values aiming at the weight value of each subordinate factor of the first factor layer; calculating and generating a weighted value based on a hierarchical analysis algorithm, then calculating the consistency ratio of the judgment matrixes of the first factor layer and the plurality of second factor layers, when the consistency ratio of the judgment matrixes of the first factor layer and the plurality of second factor layers is smaller than a preset threshold value, calculating a combined weight vector according to the first weighted value and the plurality of second weighted values, generating a target weighted value of the second factor layer for a target layer, and then acquiring and preprocessing multiple types of data of a target road in a preset time period; and finally, judging whether a bus lane is set on a target road within a preset time period according to the comprehensive evaluation value. According to the method and the device, the model is established by adopting various factors of the bus lane, and the model is analyzed and calculated by adopting a hierarchical analysis algorithm, so that the reasonability of bus lane construction is improved, and the invalid occupation of road traffic resources is further reduced.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware that is related to instructions of a computer program, and the program can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. A method for determining setting of a bus lane, the method comprising:
obtaining multiple types of factors influencing the setting of a bus lane, and constructing a hierarchical structure model based on the multiple types of factors; the hierarchical structure model at least comprises a target layer, a first factor layer and a second factor layer;
calculating a weight value of the first factor layer for the target layer to generate a first weight value;
calculating a weight value of the second factor layer for each membership factor of the first factor layer, and generating a plurality of second weight values; wherein the weighted values are generated by calculation based on a hierarchical analysis algorithm;
calculating consistency ratios of the judgment matrixes of the first factor layer and the plurality of second factor layers, and when the consistency ratios of the judgment matrixes of the first factor layer and the plurality of second factor layers are smaller than a preset threshold value, performing combined weight vector calculation according to the first weight values and the plurality of second weight values to generate target weight values of the second factor layers for the target layer;
acquiring and preprocessing various types of data of a target road in a preset time period; the multi-class data comprises data corresponding to each class of factor in the multi-class factors;
taking the product of the matrix generated after the preprocessing and the target weight value to generate a comprehensive evaluation value;
and judging whether the target road is provided with a bus lane or not within a preset time period according to the comprehensive evaluation value.
2. The method according to claim 1, wherein the determining whether the target road sets a bus lane within a preset time period according to the comprehensive evaluation value comprises:
loading a judgment grade table;
identifying a value range to which the comprehensive evaluation value belongs from the judgment grade table;
when the corresponding evaluation index of the threshold value range in the judgment grade table is a suggestion or recommendation, determining that the target road can be provided with a bus lane within a preset time period;
outputting information that the target road can be provided with a bus lane in a preset time period and sending the information to relevant departments;
alternatively, the first and second electrodes may be,
when the corresponding evaluation index of the threshold value range in the judgment grade table is not suggested or recommended, determining that the target road cannot be provided with a bus lane within a preset time period;
and outputting information that the target road cannot be provided with the bus lane within a preset time period, and sending the information to relevant departments.
3. The method of claim 1, wherein the calculating the weight value of the first factor layer for the target layer generates a first weight value, comprising:
generating a first questionnaire according to the target layer and the first factor layer, and displaying the first questionnaire;
when a selection instruction input aiming at answer options of all the questions in the first questionnaire is received, acquiring first selected answers aiming at all the questions in the first questionnaire;
generating a first decision matrix based on the first selected answer;
normalizing each row of parameters in the first judgment matrix according to rows;
and summing the matrixes normalized by columns according to rows and then performing normalization processing to generate a first weight value.
4. The method of claim 1, wherein the calculating the weight values of the second factor layer for the first factor layer generates a plurality of second weight values, comprising:
generating a second questionnaire according to the first factor layer and the second factor layer, and displaying the second questionnaire;
when a selection instruction input aiming at the answer options of all the questions in the second questionnaire is received, acquiring second selected answers aiming at all the questions in the second questionnaire;
generating a plurality of second decision matrices based on the second selected answer;
normalizing each column parameter of each of the plurality of second decision matrices by column;
and summing each matrix normalized by columns by rows and then performing normalization processing to generate a plurality of second weight values.
5. The method according to claim 3, wherein the calculating the consistency ratio of the judgment matrices of each of the first factor layer and the plurality of second factor layers comprises:
calculating a first maximum characteristic root according to the first judgment matrix;
calculating a first consistency index according to the first maximum characteristic root;
inquiring a first random consistency check value corresponding to the parameter quantity of the first judgment matrix from a preset random consistency check table;
determining a ratio of the first consistency index to the first random consistency check value as a consistency ratio corresponding to the first weight value;
and the number of the first and second groups,
calculating a plurality of second maximum feature roots according to the plurality of second judgment matrixes;
calculating a plurality of second consistency indexes according to the plurality of second maximum characteristic roots;
inquiring a plurality of second random consistency check values corresponding to the number of parameters of the plurality of second judgment matrixes from a preset random consistency check table;
determining a ratio of the plurality of second consistency indicators to the plurality of second random consistency check values as consistency ratios corresponding to the plurality of second weight values.
6. The method of claim 1, wherein the obtaining and preprocessing multiple types of data of the target road within a preset time period comprises:
acquiring various types of data of a target road in a preset time period;
and carrying out standardization and forward processing on the multi-class data to generate a matrix.
7. The method of claim 1, further comprising:
periodically acquiring a data set of relevant factors influencing the setting of a bus lane of a target road section;
preprocessing the data set of each period and then generating a matrix corresponding to each period;
after the matrix corresponding to each period is multiplied by the target weight value, generating a comprehensive evaluation value of each period;
and dynamically setting a bus lane based on the comprehensive evaluation value of each period.
8. The utility model provides a decision-making device who sets up bus lane which characterized in that, the device includes:
the model building module is used for obtaining various factors influencing the setting of the bus lane and building a hierarchical structure model based on the various factors; the hierarchical structure model at least comprises a target layer, a first factor layer and a second factor layer;
the first weight value calculating module is used for calculating a weight value of the first factor layer aiming at the target layer to generate a first weight value;
a plurality of second weight value calculation modules, configured to calculate a plurality of second weight values generated by the second factor layer for the weight values of the first factor layer; wherein the weighted values are generated by calculation based on a hierarchical analysis algorithm;
a target weight value generation module, configured to calculate consistency ratios of the determination matrices of the first factor layer and the plurality of second factor layers, and when the consistency ratios of the determination matrices of the first factor layer and the plurality of second factor layers are smaller than a preset threshold, perform combined weight vector calculation according to the first weight value and the plurality of second weight values, and then generate a target weight value of the second factor layer for the target layer;
the data acquisition module is used for acquiring and preprocessing various types of data of the target road in a preset time period; the multi-class data comprises data corresponding to each class of factor in the multi-class factors;
a comprehensive evaluation value generation module, configured to generate a comprehensive evaluation value by multiplying the preprocessed matrix by the target weight value;
and the judging module is used for judging whether the target road is provided with a bus lane within a preset time range according to the comprehensive evaluation value.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to perform the method steps according to any of claims 1-7.
10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-7.
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