CN108665097B - Freight demand simulation prediction method and device and storage medium - Google Patents
Freight demand simulation prediction method and device and storage medium Download PDFInfo
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Abstract
The invention provides a freight demand simulation prediction method, a device and a storage medium, and relates to the technical field of transportation demand simulation. The freight demand simulation and prediction method comprises the steps of firstly, calibrating an input-output row coefficient model based on trade data of each department of a first region between a basic year and each region, obtaining predicted freight value flow of the first region by using the input-output row coefficient model based on a predicted region total production value of the first region, and converting the predicted freight value flow into freight demand simulation prediction of the first region. The freight demand simulation and prediction method carries out simulation and prediction on freight demands by using the input-output model through the regional production total value of the region, improves the accuracy of the freight demand simulation and prediction, and expresses the influence of national economic development on the freight demands.
Description
Technical Field
The invention relates to the technical field of transportation demand simulation, in particular to a freight demand simulation prediction method, a freight demand simulation prediction device and a storage medium.
Background
With the continuous and rapid development of economy in China, the specialization and the refinement of the division of labor among areas, the acquisition of various production elements and the increasingly expanded marketing range of products, the freight demand of each area is vigorous, the future freight demand can be accurately predicted, and the method has important significance for the formulation of economic policies and the rapid coordinated development of economy of each area.
At present, a lot of methods are used for simulating the freight volume demand, and the traditional methods mainly comprise a time series prediction method, a regression analysis method, a system dynamics method, a gray model method and the like. Considering that the economic development of each area is different, the existing basic data types and data integrality are different, the traditional freight volume prediction method has certain applicability and limitation, and cannot reflect the economic development of each industrial department and the influence of the flow direction of goods among the areas on the freight volume demand.
Disclosure of Invention
In view of the above, an object of the embodiments of the present invention is to provide a method, an apparatus and a storage medium for simulating and predicting a freight requirement, so as to solve the above problems.
In a first aspect, an embodiment of the present invention provides a freight demand simulation and prediction method, where a input-output line coefficient model is calibrated based on trade data of each department of a first region between a basic year and each region, a predicted freight value flow of the first region is obtained by using the input-output line coefficient model based on a predicted region total production value of the first region, and the predicted freight value flow is converted into a freight demand simulation predicted amount of the first region.
In summary of the first aspect, the method for calibrating an input-output line coefficient model based on trade data of departments in a first region between a basic year and each region includes: based on the direct consumption value quantity of each department and the total yield of the departments in the trade data, obtaining a direct consumption coefficient by using a direct consumption coefficient formula; obtaining a trade coefficient by using a trade coefficient formula based on the inter-area outflow and the total inflow of each department in the trade data; and calibrating an input-output travel coefficient model based on the direct consumption coefficient and the trade coefficient.
In summary of the first aspect, before obtaining a trade coefficient by using a trade coefficient formula based on the inter-area outflow and the total inflow of each department in the trade data, the freight demand simulation prediction method further includes: obtaining a friction coefficient by using a friction coefficient formula based on freight volume data among the regions in the trade data; and obtaining the inter-area outflow corresponding to each department by using an inter-area outflow formula based on the supply demand data and the friction coefficient between the areas in the trade data.
In summary of the first aspect, the obtaining the predicted freight value movement of the first region by using the input-output row coefficient model based on the predicted regional production total value of the first region includes: acquiring the proportion of the added value of each industry of the first area in the total area production value of the first area in the basic year, and respectively multiplying the total area production value predicted by the first area by the proportion to obtain the predicted added value of each industry of the first area; based on the predicted added value and the direct consumption coefficient of each industry of the first area, obtaining total output based on a balance relation formula; and obtaining the predicted cargo value flow of the first area by utilizing the input-output row coefficient model based on the total output.
In summary of the first aspect, the converting the predicted freight value movement into the simulated freight demand forecast for the first area includes: and converting the predicted cargo value flow amount into a freight demand simulation predicted amount of the first area based on the relationship that the predicted cargo value flow amount is equal to the price multiplied by the physical amount.
In a second aspect, an embodiment of the present invention provides a freight demand simulation and prediction apparatus, where the freight demand simulation and prediction apparatus includes a calibration module, a predicted freight value flow calculation module, and a freight demand simulation and prediction amount calculation module. The calibration module is used for calibrating the input-output line coefficient model based on the trade data of each department in the first region between the basic year and each region. And the predicted cargo value flow calculation module is used for obtaining the predicted cargo value flow of the first region by utilizing the input-output row coefficient model based on the total predicted region production value of the first region. The freight demand simulation forecast amount calculation module is used for converting the forecast freight value flow amount into the freight demand simulation forecast amount of the first area.
In summary of the second aspect, the rating module includes a direct consumption coefficient calculation unit, a trade coefficient calculation unit, and an input-output coefficient model rating unit. The direct consumption coefficient calculation unit is used for obtaining a direct consumption coefficient by using a direct consumption coefficient formula based on direct consumption value amount and department total output of each department in the trade data. The trade coefficient calculation unit is used for obtaining trade coefficients by using a trade coefficient formula based on the inter-area outflow and the total inflow of each department in the trade data. The input-output row coefficient model calibration unit is used for calibrating the input-output row coefficient model based on the direct consumption coefficient and the trade coefficient.
In summary of the second aspect, the calibration module further includes a friction coefficient calculation unit and an inter-region outflow amount calculation unit. The friction coefficient calculation unit is used for obtaining a friction coefficient by using a friction coefficient formula based on the freight volume data among the areas in the trade data. The inter-region outflow amount calculation unit is configured to obtain inter-region outflow amounts corresponding to respective departments by using an inter-region outflow amount formula based on the supply demand data between the regions in the trade data and the friction coefficient.
In a second aspect, the predicted freight value flow calculation module further includes a predicted added value calculation unit, a total output calculation unit and a predicted freight value flow calculation unit for each industry. The each industry prediction added value calculating unit is used for obtaining the proportion of the added value of each industry of the first area in the total area production value of the first area in the basic year, and multiplying the total predicted area production value of the first area by the proportion to obtain the each industry prediction added value of the first area. The total output calculation unit is used for obtaining a total output based on a balance relation formula based on the industry prediction added value and the direct consumption coefficient of the first area. And the predicted cargo value flow calculation unit is used for obtaining the predicted cargo value flow of the first area by utilizing the input-output row coefficient model based on the total output.
In a third aspect, an embodiment of the present invention further provides a storage medium stored in a computer, where the storage medium includes a plurality of instructions configured to cause the computer to execute the above method.
The beneficial effects provided by the invention are as follows:
the invention provides a freight demand simulation and prediction method, a device and a storage medium, wherein the freight demand simulation and prediction method adopts regional production total value data during simulation and prediction, so that the freight demand simulation and prediction result is more accurate, and the direct relation between economic development and freight transportation volume can be reflected. Meanwhile, the freight demand simulation and prediction method utilizes the input-output model and the input-output table to obtain the predicted freight value flow quantity and converts the predicted freight value flow quantity into the freight demand simulation and prediction quantity, so that the cargo sending quantity and the cargo arrival quantity in the traditional statistics can be reflected, the communication relation between the starting point and the destination can be reflected, the total quantity of the cargos in each area can be calculated, the communication quantity between each area and other areas can be obtained, and the space communication of cargo transportation is really realized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a freight requirement simulation and prediction method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a calibration procedure of an input/output line coefficient model according to a first embodiment of the present invention;
FIG. 3 is a comparison graph of the total freight volume and the measured value of each province obtained by the freight demand simulation and prediction method according to the first embodiment of the present invention;
fig. 4 is a comparison graph of the total amount sent and the measured value of each province obtained by the freight requirement simulation prediction method according to the first embodiment of the present invention;
FIG. 5 is a comparison graph of the total arrival quantity of each province obtained by the freight requirement simulation and prediction method according to the first embodiment of the present invention and the measured value;
FIG. 6 is a block diagram of a freight requirement simulation and prediction device according to a second embodiment of the present invention;
fig. 7 is a block diagram of an electronic device according to a third embodiment of the present invention.
Icon: 100-freight requirement simulation and prediction device; 110-rating module; 120-a predicted freight value flow calculation module; 130-freight transportation demand simulation forecast calculation module; 200-an electronic device; 201-a memory; 202-a memory controller; 203-a processor; 204-peripheral interface; 205-input-output unit; 206-an audio unit; 207-display unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
First embodiment
The applicant researches and discovers that with the continuous and rapid development of economy in China, the economic communication of each region is increased, the freight volume between the regions is closely related to various economic communication volumes of each region, but the traditional freight volume demand simulation methods, such as a time sequence prediction method, a regression analysis method, a system dynamics method, a gray model method and the like, have certain applicability and limitation. For time series prediction, the required data series is less, the method is simple and easy to implement, the prediction effect on the future short-term prediction is better, actual factors influencing the change of the transportation volume cannot be reflected, and the transportation demand fluctuation caused by external factors such as economic policies cannot be coped with; the system dynamics approach can simulate the interaction between regional freight systems and economic development, improving the time series prediction model, but it does not cover enough space and network details and the parameters are difficult to statistically test. To solve the above problems, a freight requirement simulation and prediction method according to a first embodiment of the present invention is provided, please refer to fig. 1, and fig. 1 is a flowchart of a freight requirement simulation and prediction method according to a first embodiment of the present invention. The freight demand simulation and prediction method comprises the following specific steps:
step S10: and (4) based on the trade data of each department in the first region between the basic year and each region, calibrating the input production row coefficient model.
Step S20: and obtaining the predicted freight value flow of the first region by utilizing the input-output row coefficient model based on the predicted region production total value of the first region.
Step S30: and converting the predicted freight value flow amount into a freight demand simulation predicted amount of the first area.
For step S10: and (4) based on the trade data of each department in the first region between the basic year and each region, calibrating the input production row coefficient model. Considering that when freight demand simulation is performed on the future year, data of the past year required in the input-output row coefficient model cannot be directly obtained for direct calculation, and therefore the input-output row coefficient model needs to be calibrated firstly by taking the past year of the existing trade data as a basic year. As an implementation manner, please refer to fig. 2, fig. 2 is a flowchart of a input/output line coefficient model calibration step according to a first embodiment of the present invention, where the input/output line coefficient model calibration step specifically includes:
step S11: and obtaining a direct consumption coefficient by using a direct consumption coefficient formula based on the direct consumption value quantity of each department and the total production of each department in the trade data.
Step S12: and obtaining the trade coefficient by using a trade coefficient formula based on the inter-area outflow and the total inflow of each department in the trade data.
Step S13: and calibrating an input-output travel coefficient model based on the direct consumption coefficient and the trade coefficient.
For step S11, the direct consumption coefficient is formulated as
Wherein, aijMeans the value quantity, x, of goods or services in the ith product division which are directly consumed by the unit total production of the jth product division in the production and operation processijQuantity of value, X, of goods or services of the ith division consumed directly in production operations of the jth divisionjIs the total investment of the j-th department. From direct consumption coefficient aijThe formed n × n matrix a is called a direct consumption coefficient matrix, which is also directly called a direct consumption coefficient in this embodiment, and the matrix a reflects the technical-economic relations between the industry departments and the products in the input-output table. The direct consumption coefficient is the most important and basic coefficient for establishing an input-output table and a model, and is the core of the input-output model. Further, the input-output table is also called department contact balance table, which is the collectionAnd a balance table reflecting the relationship between each department and the balance proportion in the input-output analysis. The input-output analysis is one of basic methods of quantitative economics, and is a modern model method which combines economics and mathematics to be the most consistent, an input-output table is a main analysis tool of the input-output table, the input-output table is an important component of a national economy accounting system, is an important tool for carrying out national economy comprehensive balance analysis, strengthening macroscopic economy regulation and control and realizing scientific decision, is used as a powerful analysis tool, and is widely applied to the fields of production analysis, demand analysis, price and cost analysis, energy and environment analysis and the like. The input-output table contains data of inter-area trade quantity of the product, flow direction between each department, intermediate input of the product, final consumption and the like.
As an embodiment, before executing step S12, the embodiment may further include a step of obtaining the inter-region outflow amount, which includes the following specific steps:
step S11.1: and obtaining a friction coefficient by using a friction coefficient formula based on the freight volume data among the areas in the trade data.
Step S11.2: and obtaining the inter-area outflow corresponding to each department by using an inter-area outflow formula based on the supply demand data and the friction coefficient between the areas in the trade data.
For step S11.1, the friction coefficient is formulated as
Wherein the content of the first and second substances,
the volume of shipments sent to area S for area R,
the total volume of shipments to all zones for zone R,
is the total freight volume of the arrival area S,
is the total amount of transmissions (equal to the total amount of arrivals) for all of the zones.
In step S11.2, the formula of the inter-region outflow amount is
Wherein the content of the first and second substances,
is the outflow of the section i from the area R to the area S i.e. the inter-area outflow,
is the total production (total feed) of section i of region R,
for the total demand of products for section S to department i (the sum of the intermediate demand and the final demand),
is the total output of all regional departments i (equal to the total demand),
is the coefficient of friction of the department product i from region R to region S.
For step S12, namely: and obtaining the trade coefficient by using a trade coefficient formula based on the inter-area outflow and the total inflow of each department in the trade data. The trade coefficient formula is
Wherein the content of the first and second substances,
is the outflow of the section i from the region R to the region S, i.e. the inter-region flowOutput quantity;
to the extent that the proportion of i industrial products flowing into S is from region R,
i.e. the total inflow.
Step S13 should be performed next: and calibrating an input-output travel coefficient model based on the direct consumption coefficient and the trade coefficient. The input-output row coefficient model is CAX + CF + E-M ═ X, where X is the total output of all regions, i.e., the predicted freight value flow, F is the final demand of each region, E, M are the outlet and inlet vectors of each region, respectively, a is the direct consumption coefficient of all regions, and C is the trade coefficient matrix. CAX is the middle input part of the basic annual regional value flow table, which is a square matrix with 1302 rows and 1302 columns and represents the mutual value flow in the production process of 31 regions and 42 departments in the direct prefecture city.
According to the execution sequence of the present embodiment, step S20 should be executed next: and obtaining the predicted freight value flow of the first region by utilizing the input-output row coefficient model based on the predicted region production total value of the first region. As an embodiment, step S20 may include the following substeps:
step S21: and acquiring the proportion of the added value of each industry of the first area in the total area production value of the first area in the basic year, and multiplying the total area production value predicted by the first area by the proportion to obtain the predicted added value of each industry of the first area.
Step S22: and obtaining total output based on a balance relation formula based on the industry prediction added value and the direct consumption coefficient of the first area.
Step S23: and obtaining the predicted cargo value flow of the first area by utilizing the input-output row coefficient model based on the total output.
Before executing step S21, it is also necessary to satisfy the formula of balance relationship, X ═ N (I-a)-1To obtainThe industry increase value of the basic year, wherein A is the direct consumption coefficient aijAnd forming an N multiplied by N matrix, wherein X is a column vector of total values of all departments in each region, I is a unit matrix, and N is an industry increase value column vector. And calculating the industry increase value N of each department in each region of the basic year by using the obtained middle investment of each region of the basic year and the total output of 42 departments in the 31 region in the investment and output table of the existing region of the basic year by using the formula.
Regarding step S22, there is the following relationship between the partition GDP and the partition all-department added value: the GDP for an area is equal to the sum of the corresponding added values for all departments in the area. And each area is independently seen, the ratio obtained by dividing the increment value of each industry in each area of the basic year by the GDP of the basic year in each area is multiplied by the GDP of the future year in all the areas, and therefore the predicted increment value of each industry in each area of the future year is obtained.
In step S23, based on the stability of the direct consumption coefficient, assuming that the direct consumption coefficient is constant for a certain period of time, the predicted increase value of each industry in the future is used, and the input-output column balance relation X is equal to N (I-a)-1And calculating total output X of the previous year, and calculating the predicted freight value flow of the first area by using an input output line coefficient model CAX + CF + E-M ═ X.
According to the implementation sequence of the present embodiment, step S30 should be performed next: and converting the predicted freight value flow amount into a freight demand simulation predicted amount of the first area. As an embodiment, the present step includes: and converting the predicted cargo value flow amount into a freight demand simulation predicted amount of the first area based on the relationship that the predicted cargo value flow amount is equal to the price multiplied by the physical amount.
To better illustrate the accuracy of the simulated forecast of the freight demand obtained by the simulated forecast method of freight demand according to the first embodiment of the present invention, please refer to fig. 3, fig. 4, and fig. 5, where fig. 3, fig. 4, and fig. 5 are respectively a comparison graph of the total freight quantity of each province, the total delivery quantity of each province, and the total arrival quantity of each province obtained by the simulated forecast method of freight demand according to the first embodiment of the present invention with an actual measurement value. Wherein, a line with a smaller starting time in fig. 3, a larger starting time in fig. 4, and a smaller starting time in fig. 5 is a calculated value.
Second embodiment
In order to cooperate with the method for simulating and predicting freight requirements provided in the first embodiment of the present invention, a device 100 for simulating and predicting freight requirements is also provided in the second embodiment of the present invention. Referring to fig. 6, fig. 6 is a block diagram of a freight requirement simulation and prediction apparatus according to a second embodiment of the present invention.
The freight demand simulation prediction apparatus 100 includes a calibration module 110, a predicted freight value flow amount calculation module 120, and a freight demand simulation prediction amount calculation module 130.
A rating module 110, configured to rate the input production line coefficient model based on trade data of each department in the first area between the basic year and each area.
The value calculation module 110 includes a direct consumption coefficient calculation unit, a trade coefficient calculation unit, and an input-output line coefficient model calibration unit. The direct consumption coefficient calculation unit is used for obtaining a direct consumption coefficient by using a direct consumption coefficient formula based on direct consumption value amount and department total output of each department in the trade data. The trade coefficient calculation unit is used for obtaining trade coefficients by using a trade coefficient formula based on the inter-area outflow and the total inflow of each department in the trade data. The input-output row coefficient model calibration unit is used for calibrating the input-output row coefficient model based on the direct consumption coefficient and the trade coefficient.
Optionally, the calibration module 110 may further include a friction coefficient calculation unit and an inter-region outflow amount calculation unit. The friction coefficient calculation unit is used for obtaining a friction coefficient by using a friction coefficient formula based on the freight volume data among the areas in the trade data. The inter-region outflow amount calculation unit is configured to obtain inter-region outflow amounts corresponding to respective departments by using an inter-region outflow amount formula based on the supply demand data between the regions in the trade data and the friction coefficient.
And a predicted cargo value flow calculation module 120, configured to obtain the predicted cargo value flow of the first region by using the input-output row coefficient model based on the total predicted region production value of the first region.
The predicted cargo value flow calculation module 120 includes a prediction added value calculation unit, a total output calculation unit, and a predicted cargo value flow calculation unit for each industry. The each industry prediction added value calculating unit is used for obtaining the proportion of the added value of each industry of the first area in the total area production value of the first area in the basic year, and multiplying the total predicted area production value of the first area by the proportion to obtain the each industry prediction added value of the first area. The total output calculation unit is used for obtaining a total output based on a balance relation formula based on the industry prediction added value and the direct consumption coefficient of the first area. And the predicted cargo value flow calculation unit is used for obtaining the predicted cargo value flow of the first area by utilizing the input-output row coefficient model based on the total output.
And a freight demand simulation forecast amount calculation module 130, configured to convert the predicted freight value flow amount into a freight demand simulation forecast amount for the first region.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method, and will not be described in too much detail herein.
Third embodiment
In order to implement the step counting method, the third embodiment of the present invention provides an electronic device 200. Referring to fig. 7, fig. 7 is a block diagram of an electronic device according to a third embodiment of the present invention.
The electronic device 200 may include the freight requirement simulation prediction apparatus 100, a memory 201, a storage controller 202, a processor 203, a peripheral interface 204, an input/output unit 205, an audio unit 206, and a display unit 207.
The memory 201, the memory controller 202, the processor 203, the peripheral interface 204, the input/output unit 205, the audio unit 206, and the display unit 207 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The freight requirement simulation and prediction device 100 comprises at least one software functional module which can be stored in the memory 201 in the form of software or firmware (firmware) or solidified in an Operating System (OS) of the freight requirement simulation and prediction device 100. The processor 203 is configured to execute an executable module stored in the memory 201, such as a software functional module or a computer program included in the freight requirement simulation prediction apparatus 100.
The Memory 201 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 201 is used for storing a program, the processor 203 executes the program after receiving an execution instruction, and the method executed by the server defined by the flow process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 203, or implemented by the processor 203.
The processor 203 may be an integrated circuit chip having signal processing capabilities. The Processor 203 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor 203 may be any conventional processor or the like.
The peripheral interface 204 couples various input/output devices to the processor 203 as well as to the memory 201. In some embodiments, the peripheral interface 204, the processor 203, and the memory controller 202 may be implemented in a single chip. In other examples, they may be implemented separately from the individual chips.
The input and output unit 205 is used for providing input data for a user to realize the interaction of the user with the server (or the local terminal). The input/output unit 205 may be, but is not limited to, a mouse, a keyboard, and the like.
The audio unit 206 provides an audio interface to the user, which may include one or more microphones, one or more speakers, and audio circuitry.
The display unit 207 provides an interactive interface (e.g., a user operation interface) between the electronic device 200 and a user or is used to display image data for user reference. In this embodiment, the display unit 207 may be a liquid crystal display or a touch display. In the case of a touch display, the display can be a capacitive touch screen or a resistive touch screen, which supports single-point and multi-point touch operations. Supporting single-point and multi-point touch operations means that the touch display can sense touch operations from one or more locations on the touch display at the same time, and the sensed touch operations are sent to the processor 203 for calculation and processing.
It is to be understood that the configuration shown in fig. 7 is merely exemplary, and the electronic device 200 may include more or fewer components than shown in fig. 7, or may have a different configuration than shown in fig. 7. The components shown in fig. 7 may be implemented in hardware, software, or a combination thereof.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method, and will not be described in too much detail herein.
In summary, embodiments of the present invention provide a method, an apparatus, and a storage medium for simulating and predicting a freight demand, where the method uses area production total value data during simulation and prediction, so that a freight demand simulation and prediction result is more accurate, and a direct relationship between economic development and freight transportation volume can be reflected. Meanwhile, the freight demand simulation and prediction method utilizes the input-output model and the input-output table to obtain the predicted freight value flow quantity and converts the predicted freight value flow quantity into the freight demand simulation and prediction quantity, so that the cargo sending quantity and the cargo arrival quantity in the traditional statistics can be reflected, the communication relation between the starting point and the destination can be reflected, the total quantity of the cargos in each area can be calculated, the communication quantity between each area and other areas can be obtained, and the space communication of cargo transportation is really realized.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (5)
1. A freight demand simulation prediction method is characterized by comprising the following steps:
based on the trade data of each department in the first region between the basic year and each region, and based on the direct consumption value amount and the department total output of each department in the trade data, obtaining a direct consumption coefficient by using a direct consumption coefficient formula; obtaining a friction coefficient by using a friction coefficient formula based on freight volume data among the regions in the trade data; the friction coefficient is expressed as
Wherein, in the step (A),
the volume of shipments sent to area S for area R,
the total volume of shipments to all zones for zone R,
is the total freight volume of the arrival area S,
is the total transmission volume of the entire area; obtaining inter-area outflow quantities corresponding to departments by using an inter-area outflow quantity formula based on supply demand data and the friction coefficient between the areas in the trade data; said zoneThe inter-domain outflow is formulated as
Wherein, in the step (A),
is the outflow of the section i from the area R to the area S i.e. the inter-area outflow,
is the total output of the section i of region R,
for the total product demand of section i for region S,
is the total output of all the regional departments i,
is the coefficient of friction of the department product i from region R to region S; obtaining a trade coefficient by using a trade coefficient formula based on the inter-area outflow and the total inflow of each department in the trade data, wherein the trade coefficient formula is
Wherein, in the step (A),
is the outflow of the section i from the region R to the region S, i.e., the inter-region outflow;
to the extent that the proportion of i industrial products flowing into S is from region R,
is the total inflow; based on the direct eliminationThe consumption coefficient and the trade coefficient rate an input-output line coefficient model, wherein the input-output line coefficient model is CAX + CF + E-M = X,Xthe cargo value movement is predicted for the total production of all regions,Ffor the ultimate needs of each of the zones,E、Mrespectively the outlet and inlet vectors of each zone,Afor the direct consumption coefficient of all the areas,Cis a trade coefficient matrix;
acquiring the proportion of the added value of each industry of the first area in the total area production value of the first area in the basic year, and respectively multiplying the total area production value predicted by the first area by the proportion to obtain the predicted added value of each industry of the first area; according to the stability of the direct consumption coefficient, assuming that the direct consumption coefficient is constant in a certain period, utilizing the obtained predicted increase value of each industry in the first area of the future year and according to the input-output column balance relational expression
Wherein A is the direct consumption coefficient a ij A matrix of n x n is formed,Xcalculating total output X of the future year for column vectors of total output values of all departments in each region, wherein I is a unit matrix, N is an industry increase value column vector, and calculating by utilizing an input production row coefficient model CAX + CF + E-M = X to obtain the predicted goods value flow of the first region;
and converting the predicted cargo value flow amount into a freight demand simulation predicted amount of the first area based on the relationship that the predicted cargo value flow amount is equal to the price multiplied by the physical amount.
2. The simulation prediction method for demand for freight according to claim 1, wherein before the obtaining of the trade coefficient by using a trade coefficient formula based on the inter-area outflow and the total inflow of each department in the trade data, the simulation prediction method for demand for freight further comprises:
obtaining a friction coefficient by using a friction coefficient formula based on freight volume data among the regions in the trade data;
and obtaining the inter-area outflow corresponding to each department by using an inter-area outflow formula based on the supply demand data and the friction coefficient between the areas in the trade data.
3. A freight demand simulation prediction device, characterized by comprising:
the calibration module is used for obtaining a direct consumption coefficient by using a direct consumption coefficient formula based on the trade data of each department in the first region between the basic year and each region and based on the direct consumption value amount and the total department output of each department in the trade data; obtaining a friction coefficient by using a friction coefficient formula based on freight volume data among the regions in the trade data; the friction coefficient is expressed as
Wherein, in the step (A),
the volume of shipments sent to area S for area R,
the total volume of shipments to all zones for zone R,
is the total freight volume of the arrival area S,
is the total transmission volume of the entire area; obtaining inter-area outflow quantities corresponding to departments by using an inter-area outflow quantity formula based on supply demand data and the friction coefficient between the areas in the trade data; the flow rate between the areas is expressed as
Wherein, in the step (A),
is the outflow of the section i from the area R to the area S i.e. the inter-area outflow,
is the total output of the section i of region R,
for the total product demand of section i for region S,
is the total output of all the regional departments i,
is the coefficient of friction of the department product i from region R to region S; obtaining a trade coefficient by using a trade coefficient formula based on the inter-area outflow and the total inflow of each department in the trade data, wherein the trade coefficient formula is
Wherein, in the step (A),
is the outflow of the section i from the region R to the region S, i.e., the inter-region outflow;
to the extent that the proportion of i industrial products flowing into S is from region R,
is the total inflow; rating an input-output line coefficient model based on the direct consumption coefficient and the trade coefficient, the input-output line coefficient model being CAX + CF + E-M = X, wherein,Xthe cargo value movement is predicted for the total production of all regions,Ffor each regionThe final requirement of (a) to (b),E、Mrespectively the outlet and inlet vectors of each zone,Afor the direct consumption coefficient of all the areas,Cis a trade coefficient matrix;
the predicted cargo value flow calculation module is used for acquiring the proportion of the added value of each industry of the first area in the basic year to the total area production value of the first area, and multiplying the total predicted area production value of the first area by the proportion to obtain the predicted added value of each industry of the first area; according to the stability of the direct consumption coefficient, assuming that the direct consumption coefficient is constant in a certain period, utilizing the obtained predicted increase value of each industry in the first area of the future year and according to the input-output column balance relational expression
Calculating total output X of the previous year, and calculating to obtain the predicted freight value flow of the first area by utilizing an input-output line coefficient model CAX + CF + E-M = X;
and the freight demand simulation forecast amount calculation module is used for converting the predicted freight value flow amount into the freight demand simulation forecast amount of the first area based on the relation that the predicted freight value flow amount is equal to the price multiplied by the physical quantity.
4. The freight demand simulation prediction apparatus according to claim 3, wherein the rating module further includes:
a friction coefficient calculation unit, configured to obtain a friction coefficient by using a friction coefficient formula based on the transportation data between the regions in the trade data;
and the inter-region outflow amount calculating unit is used for obtaining the inter-region outflow amount corresponding to each department by using an inter-region outflow amount formula based on the supply demand data and the friction coefficient between the regions in the trade data.
5. A computer-readable storage medium having computer program instructions stored thereon which, when read and executed by a processor, perform the steps of the method of any of claims 1-2.
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