CN115271260A - Road-rail transport capacity prediction method, device, equipment and readable storage medium - Google Patents
Road-rail transport capacity prediction method, device, equipment and readable storage medium Download PDFInfo
- Publication number
- CN115271260A CN115271260A CN202211169408.2A CN202211169408A CN115271260A CN 115271260 A CN115271260 A CN 115271260A CN 202211169408 A CN202211169408 A CN 202211169408A CN 115271260 A CN115271260 A CN 115271260A
- Authority
- CN
- China
- Prior art keywords
- highway
- demand
- points
- point
- railway
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 238000003860 storage Methods 0.000 title claims abstract description 9
- 238000005065 mining Methods 0.000 claims abstract description 23
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 20
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 230000002441 reversible effect Effects 0.000 claims abstract description 9
- 230000009191 jumping Effects 0.000 claims description 25
- 238000010276 construction Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 9
- 230000015654 memory Effects 0.000 claims description 9
- 230000006870 function Effects 0.000 claims description 8
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 239000000126 substance Substances 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- 238000007418 data mining Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 9
- 238000009826 distribution Methods 0.000 description 7
- 230000008901 benefit Effects 0.000 description 6
- 230000000694 effects Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000012216 screening Methods 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 230000003247 decreasing effect Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 239000004956 Amodel Substances 0.000 description 1
- 241000728173 Sarima Species 0.000 description 1
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000007787 long-term memory Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 230000006403 short-term memory Effects 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Databases & Information Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Mathematical Physics (AREA)
- Remote Sensing (AREA)
- Fuzzy Systems (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method, a device and equipment for forecasting combined transportation capacity of a highway and a railway and a readable storage medium, and relates to the technical field of forecasting combined transportation capacity of the highway and the railway. The method comprises the steps of obtaining road goods source data and preprocessing the data; clustering the preprocessed road goods source data by adopting a DBSCAN clustering algorithm based on the optimal contour coefficient; performing reverse geocoding matching on the clustering result of the single-day highway freight demand points; mining the geographic position matching result by adopting a frequent item set mining algorithm; constructing a hub and spoke type highway and railway combined transportation hub node site selection model, and solving to obtain highway and railway combined transportation hub points and service areas thereof; excavating freight characteristics in a service area of each highway-railway combined transport pivot point; and (4) extracting the cargo quantity of each type of service cargo at each highway-railway combined transportation pivot point from the highway cargo source data, and predicting the cargo quantity at the next moment by adopting an integrated sliding average autoregressive model. The method can effectively reduce the prediction cost and improve the prediction precision of the combined transportation capacity of the highway and the railway.
Description
Technical Field
The invention relates to the technical field of highway-railway combined transportation volume prediction, in particular to a highway-railway combined transportation volume prediction method, a device, equipment and a readable storage medium.
Background
The transportation system is a complex dynamic system, and the cargo capacity prediction is always a research difficulty. Currently, there are several prediction methods. The method is characterized in that a time sequence prediction method based on statistics is adopted, common methods comprise moving average, exponential smoothing and the like, and Song light bisection respectively predicts the railway freight volume by utilizing a combined model of three mainstream methods, so that the combined model is proved to improve the prediction precision relative to a single model. The method has the advantages that a Holt-Winter multiplication model is constructed by Yingyin decoction to predict the monthly freight volume of the railway, and the effectiveness of the model is proved after the model is compared with a traditional prediction model. The strong-interpolation line predicts subway passenger flow by using a periodic differential integration moving average autoregressive model (SARIMA) model and compares the subway passenger flow with a neural network model. The time series prediction method based on statistics is simple in modeling and capable of quickly calculating results, but the requirement on data is high.
And the other type is a prediction method based on machine learning, two latitude characteristics of time and weather are added into Bhattacharya and the like, and a regional taxi quantity prediction model is constructed by utilizing a Bayes classifier. Yang et al propose a method for predicting traffic flow by using long-short term memory neural network and get better effect. Guohongpeng and the like establish a short-term freight volume prediction model based on a bidirectional long-term and short-term memory network, and compare the short-term freight volume prediction model with model prediction results of random forests, support vector machines and the like to obtain the conclusion that the model has strong generalization capability and prediction accuracy. Tangxue and the like respectively carry out single-step and multi-step prediction on the monthly goods transportation volume and the daily goods transportation volume of the railway by utilizing a gate control circulation unit depth network, and comparison experiments prove that the model can effectively predict the short-term goods transportation volume. The prediction method based on machine learning generally has higher prediction accuracy, but the training time cost is higher, and the generalization capability is not strong due to easy overfitting.
The analysis of the existing research literature shows that the existing literature generally researches the highway-railway combined transportation by constructing an ideal model abstract reality condition to solve an optimal scheme, a large amount of extensive data support is lacked, and the characteristics of regional scattered source goods types, goods quantity and space distribution are ignored, so that the accuracy of forecasting the highway-railway combined transportation quantity can be influenced, and reliable data reference can not be provided for railway department decision making.
Disclosure of Invention
In view of the above-mentioned deficiencies in the prior art, the present invention provides a method, an apparatus, a device and a readable storage medium for predicting the transportation capacity of a highway and railway.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
in a first aspect, the invention provides a highway-railway combined transportation volume prediction method, which comprises the following steps:
s1, obtaining road goods source data and preprocessing the data;
s2, clustering the preprocessed road cargo source data by adopting a DBSCAN clustering algorithm based on an optimal contour coefficient to obtain a clustering result of the single-day road cargo demand points;
s3, performing reverse geocode matching on the single-day highway freight demand point clustering result to obtain a geographic position matching result;
s4, mining the geographic position matching result by adopting a frequent item set mining algorithm to obtain a hot spot area required by road freight;
s5, selecting alternate points of the highway-railway combined transportation hub nodes, constructing a hub-spoke type highway-railway combined transportation hub node site selection model by combining with a highway freight demand hot spot area, and solving to obtain the highway-railway combined transportation hub points and service areas thereof;
s6, excavating freight characteristics in the service area of each highway-railway combined transportation pivot point to obtain the service cargo type of each highway-railway combined transportation pivot point;
and S7, extracting the cargo quantity of each type of service cargo of each highway-railway combined transportation pivot point from the highway cargo source data, and predicting the cargo quantity at the next moment by adopting an integrated sliding average autoregressive model.
Optionally, step S2 specifically includes the following sub-steps:
s21, setting a value range and a transformation step length of an initial neighborhood radius and a minimum point number;
s22, randomly selecting an initial demand point from demand set points;
s23, judging whether the demand point with the minimum point number exists in the neighborhood radius range of the demand point;
if yes, clustering all the existing demand points into a cluster, and skipping to the step S24;
otherwise, judging the demand point as a noise point, and skipping to the step S22;
s24, traversing each demand point in the cluster, and judging whether the demand point with the minimum point number exists in the neighborhood radius range of the selected demand point;
if yes, merging all the existing demand points into the cluster, and jumping to the step S25;
otherwise, directly jumping to the step S25;
s25, judging whether the clusters have unrepeated demand points or not;
if yes, jumping to step S24;
otherwise, jumping to step S26;
s26, judging whether unprocessed demand points exist in the demand set points;
if yes, jumping to step S22;
otherwise, jumping to step S27;
s27, judging whether the neighborhood radius and the minimum point number reach the maximum value or not;
if yes, jumping to step S28;
otherwise, updating the neighborhood radius and the minimum point number according to the conversion step length, and jumping to the step S22;
s28, calculating the contour coefficient of each cluster and marking cluster marks to obtain the longitude and latitude coordinates of each demand point and the cluster label of each demand point.
Optionally, the calculation method of the contour coefficient of each cluster is as follows:
calculating the spherical distance from each demand point to other demand points in the cluster to which the demand point belongs according to the longitude and latitude coordinates of the demand points, and calculating the average distance;
calculating the spherical distance from each demand point to all demand points in other clusters according to the longitude and latitude coordinates of the demand points, and calculating the average distance;
calculating the contour coefficient of each demand point according to the average distance from each demand point to other demand points in the cluster to which the demand point belongs and the average distance from each demand point to all demand points in other clusters;
and averaging the contour coefficients of all the demand points in each cluster to obtain the contour coefficient of each cluster.
Optionally, the calculation formula of the spherical distance is:
wherein, the first and the second end of the pipe are connected with each other,dis the spherical distance of the two required points,ris the radius of the earth, and is,
the latitude of the two demand points is,
longitude for both demand points.
Optionally, the calculation formula of the profile coefficient of each demand point is:
wherein the content of the first and second substances,
is as followsiThe profile factor of each of the demand points,
is as followsiThe average distance of a demand point to other demand points within its cluster,
is as followsiThe minimum average distance from each demand point to all demand points in other clusters;
is as followsiEach demand point going to others in other clustersjAverage distance of individual demand points.
Optionally, the method for constructing the hub and spoke type road-rail transport hub node site selection model includes:
the method comprises the steps of constructing a hub and rail transport hub node addressing model by taking the minimum of highway and railway transport construction operation cost and transport cost as an objective function, taking the maximum number of construction pivot point points, the requirement of each highway transport requirement point is met by one transport pivot point, the transport volume of the requirement points does not exceed the total requirement quantity of the requirement points, all transport requirements of the requirement points are met, the transport volume of the transport pivot point is equal to the sum of the transport volume of each requirement point to the pivot point, the total collection transport volume of selected transport pivot alternative points does not exceed the maximum transport capacity of the transport pivot point, whether the pivot points provide transport services for the requirement points or not, whether alternative pivot point is selected, the quantity of goods transported from the requirement points to another requirement point and the quantity of goods transported to the pivot points as constraint conditions.
Optionally, the hub node site selection model of the spoke type highway-railway combined transport hub specifically includes:
s.t.
wherein, the first and the second end of the pipe are connected with each other,
is a highway-railway transport hub alternate point set,
the cost is fixed for the construction of the hub,ffor the transfer of the handling charges per unit of cargo volume at the alternative pivot point,
for selecting points for highway-railway combined transportation hubjThe maximum transport capacity of the transport means (c),
a 0-1 decision variable for whether the alternative pivot point is selected,
in order to be a set of demand points for road transportation,
for the demand points of road transportationiAlternative points to highway-railway combined transport hubjThe amount of cargo to be transported is,
for selection point of road-rail transport hubjWhether to the highway transportation demand pointiA 0-1 decision variable for providing intermodal services,
is disclosedRoad transportation demand pointiAlternative point of road-rail transport hubjThe spherical distance of (a) is greater than (b),Cis the unit transportation cost;Sthe maximum number of the pivot points is constructed,
for highway transportation demand pointsiThe total volume of traffic of (2) is,
for transportation to alternate points of road-rail transport hubjThe amount of cargo.
In a second aspect, the present invention provides a device for predicting the transportation capacity of a highway/railway combined transport, including:
the data preprocessing module is used for acquiring the highway goods source data and preprocessing the data;
the data clustering module is used for clustering the preprocessed road cargo source data by adopting a DBSCAN clustering algorithm based on an optimal contour coefficient to obtain a clustering result of the single-day road cargo demand points;
the data matching module is used for carrying out reverse geocode matching on the clustering result of the single-day highway freight demand points to obtain a geographic position matching result;
the data mining module is used for mining the geographic position matching result by adopting a frequent item set mining algorithm to obtain a highway freight requirement hot spot area;
the model building module is used for selecting alternative points of the highway-railway combined transportation hub nodes, building a hub-spoke type highway-railway combined transportation hub node site selection model by combining with a highway freight demand hot spot area, and solving to obtain the highway-railway combined transportation hub points and service areas thereof;
the freight characteristic mining module is used for mining freight characteristics in the service area of each highway-railway combined transportation pivot point to obtain the service cargo type of each highway-railway combined transportation pivot point;
and the cargo quantity prediction module is used for extracting the cargo quantities of various types of service cargos at the highway and railway transportation pivot points from the highway cargo source data and predicting the cargo quantity at the next moment by adopting an integrated moving average autoregressive model.
In a third aspect, the present invention provides a device for predicting the transportation capacity of a highway/railway combined transport, including: a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the method for predicting the transportation volume of the highway-railway transportation.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, the computer program, when being executed by a processor, implementing the steps of the method for predicting the transportation volume of a highway and a railway as described above.
The invention has the following beneficial effects:
according to the method, firstly, the regional freight characteristics are analyzed by using the freight source data, then, the highway freight demand gathering region is identified by a clustering and frequent item set mining method, on the basis, a hub-and-spoke type highway-railway transport hub node site selection model is constructed and solved to obtain the distribution position and the service region of the highway-railway transport hub node, the type of the suitable railway freight in the region is mined, and finally, the freight quantity of the next moment is predicted according to the freight quantity of each type of service freight at each highway-railway transport hub node, so that the prediction cost can be effectively reduced, and the prediction precision of the highway-railway transport quantity is improved.
Drawings
Fig. 1 is a schematic flow chart illustrating a method for predicting the transportation capacity of a highway and railway in embodiment 1 of the present invention;
FIG. 2 is a schematic flow chart of step S2 in embodiment 1 of the present invention;
fig. 3 is a schematic view of a highway-railway combined transportation mode in embodiment 1 of the present invention;
fig. 4 is a schematic structural diagram of a device for predicting the transportation capacity of a highway and railway in embodiment 2 of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined by the appended claims, and all changes that can be made by the invention using the inventive concept are intended to be protected.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a method for predicting a combined transportation capacity of a highway and a railway, including the following steps S1 to S7:
s1, obtaining road goods source data and preprocessing the data;
in an optional embodiment of the invention, the highway cargo source data is firstly obtained, and by taking the original data of the highway trunk transportation online transaction platform in China as an example, when the original data is delivered by a shipper, a json character string format with a timestamp directly returned by the platform is low in readability and difficult to directly process, so that the json character string is analyzed and processed by using SQL language, 380 pieces of arrival cargo source data and 330 pieces of sending cargo source data which have 58 fields are respectively obtained, and are respectively stored in new tables Arr and Dep in a MySQL database through database management software Navicat for MySQL, so that the direct data calling and the operation of increasing, deleting and revising are facilitated.
According to the method, after the road source data are acquired, data preprocessing is needed, fields with unknown meanings, excessive null values or useless values are deleted, and related fields such as source creation time, creation timestamp, delivery place, destination, cargo type, cargo name, required vehicle length, required transportation volume or required capacity, OD longitude and latitude coordinates and the like are reserved, wherein the fields are shown in table 1.
TABLE 1 original data field name, meaning and data type,
Then, except for longitude and latitude, for the situations that repeated delivery may exist, abnormal values exist in the source of goods, and the like, deletion operation needs to be performed on such data lines in advance, and the determination conditions are respectively set as follows:
(1) The repeated goods source means that a certain shipper delivers the same goods for multiple times, the times of the situation are many, the reason is that a shipper is in danger of delivering the goods, or misoperation is caused, or the quantity of the goods in the lot is too much, and multiple trucks are required to transport the goods, so that the repeated goods source with the same five fields of the shipper id, the type of the secondary goods, the departure area county, the destination area county and the quantity of the goods is deleted, and a row of data with the minimum timestamp (timestamp) is reserved;
(2) The method comprises the steps that a shipper fills in the weight or volume of goods during shipment, most shippers fill in the weight of the goods, the shipper fills in a volume field only when the goods belong to light-weight goods, two fields of part of shippers are filled together, the number of goods sources which only fill in the volume and have the weight of 0 is counted to be smaller, and the data lines are deleted for the convenience of subsequent calculation;
(3) Data rows with overlarge weight (weight) are deleted, part of owners of goods are enterprises or logistics companies, the shipment volume of the owners of goods is far larger than that of the owners of the goods, the owners of the goods can issue all the transportation volume to a source of the goods at one time, and the data belong to normal data. Meanwhile, there is a case where a part of the shipments are randomly filled, and here, data with a shipment weight of 999 or more is defined as an abnormality, and the row is deleted.
Finally, the data is preprocessed by the following data conversion and field processing:
(1) Converting the delivery timestamp of each line of the source data into a time format like "2021/6/25 07;
(2) The above data were summed for cargo weight in hours according to the "day" and "hour" fields and sorted in ascending order by time stamp.
(3) In order to quantify the increasing and decreasing trend of the original data, a field trend is added, which means the increasing and decreasing trend of the demand at the moment and is denoted as tr, and as shown in the following formula, the trend is increasing when tr =1, otherwise, the trend is decreasing. Where represents the amount of cargo at that moment, defining that the value of tr is also 0 when t = 0.
Finally, a data table with the size of (514, 7) is obtained, and the fields of the data table are "datatime", "weight", "county", "month", "day", "hour" and "trend", respectively.
S2, clustering the preprocessed road cargo source data by adopting a DBSCAN clustering algorithm based on an optimal contour coefficient to obtain a clustering result of the single-day road cargo demand points;
in an alternative embodiment of the present invention, the present invention first explains the relevant principle of the DBSCAN clustering algorithm.
DBSCAN (sensitivity-Based Spatial Clustering of Applications with Noise) is a Spatial Clustering algorithm Based on Density. By defining a maximum set of density-connected points, the algorithm can divide an area with a certain density into one cluster, and can find clusters of arbitrary shapes in spatial data with noise. The DBSCAN algorithm has the advantages of no need of setting the number of clusters a priori, capability of dividing a data set with a complex shape, discovery of noise and abnormal points in data and the like, but good and bad clustering effect and scanning neighborhood radius (eps) And the minimum number of contained points (min) in the neighborhoodPts) The two parameters are closely related and need to be adjusted and optimized according to actual problems and clustering results.
The definition of density in DBSCAN is a certain radiusepsThe number of points included in the range, i.e. a certain spatial distance range, in the present inventionepsNumber of freight demand points min for inner roadsPtsBased on this, the concept is set as follows:
data points are first classified into three categories:
(1) Core point: if there is a certain demand point n, its radiusepsWithin a circular range of (A), including at least minPtsN points are core objects if other demand points are provided;
(2) Boundary points are as follows: if the demand point n is a core object, and the m point is in the neighborhood range and is not the core object, the demand point m is called as the boundary point of the demand point n;
(3) Noise point: and the points in all the demand point sets which do not belong to the core point or the boundary point do not belong to any cluster in the clustering result.
Definition of the relationship between data points based on density:
(1) The density is up to: for a set of demand points N for shipping, if m is at NepsIn the radius neighborhood, if n points are core points, the direct density from the n points to the m points is directly reached;
(2) The density can reach: for a freight demand point N, there is a series of sample points
Wherein the direct density of two adjacent demand points can be reached, it can be called
From
The density can be reached;
(3) Density connection: for a freight demand point set, if an s point respectively reaches the density of n points and m points, n is connected with m;
(4) Clustering: the clustering result comprises a set of all density-connected demand points.
The clustering effect of the DBSCAN algorithm depends on the radius of two parameter fieldsepsAnd minimum points minPtsWhen the parameter is the minimum point minPtsRadius of area at the time of fixationepsThe unreasonable setting of the cluster can cause excessive or too few core points, and directly influence the number of clusters to be too small or too large; radius of field of parameterepsFixed time, minimum number of points minPtsThe unreasonable arrangement of the cluster also directly influences the judgment of the core points in the cluster and the cluster quantity. In the prior art, the parameter is determined by acquiring the number of stable clustering clusters according to the experience of practical problems or continuously adjusting the parameter, but the time cost of manual trial and error is higher, so that the invention inputs the parameter within a certain range by introducing an index 'profile coefficient' for evaluating a clustering result, returns the profile coefficient until finding the input parameter corresponding to the maximum profile coefficient, and further obtains a better clustering result.
As shown in fig. 2, step S2 of the present invention specifically includes the following sub-steps:
s21, settingRadius of the beginning regionepsAnd minimum number of points minPtsAnd the corresponding transformation step sizes L1 and L2;
s22, randomly selecting an initial demand point n from the demand set points;
s23, judging the neighborhood radius of the demand point nepsWhether there is a minimum number of points min within the rangePtsA number of other demand points;
if yes, clustering all the existing demand points into a cluster N, and skipping to the step S24;
otherwise, judging the demand point as a noise point, and jumping to the step S22;
s24, traversing other demand points in the cluster N
Judging the selected demand point
Neighborhood radius of (2)epsWhether there is a minimum number of points min within the rangePtsA number of other demand points;
if yes, merging all the existing demand points into the cluster N, and jumping to the step S25;
otherwise, directly jumping to the step S25;
s25, judging whether the demand points which are not traversed exist in the cluster N or not;
if yes, jumping to the step S24;
otherwise, jumping to step S26;
s26, judging whether unprocessed demand points exist in the demand set points;
if yes, jumping to step S22;
otherwise, jumping to step S27;
s27, judging the radius of the neighborhoodepsAnd minimum points minPtsWhether the maximum value is reached;
if yes, jumping to step S28;
otherwise, the neighborhood radius and the minimum point number are updated according to the conversion step length, namelyeps=eps+L1，minPts=minPts+ L2, and go to step S22;
s28, calculating the contour coefficient of each cluster and marking cluster marks to obtain the longitude and latitude coordinates of each demand point and the cluster label of each demand point.
Specifically, the contour Coefficient (Silhouette coeffient) provided by the invention is an evaluation mode for evaluating the quality of the clustering effect. By comparing the similarity of samples in clusters
And inter-cluster sample similarity
And evaluating the reasonable degree of each sample in the clustering result belonging to the current cluster.
The calculation method for calculating the contour coefficient of each cluster comprises the following steps:
calculating the spherical distance from each demand point to other demand points in the cluster to which the demand point belongs according to the longitude and latitude coordinates of the demand points, and calculating the average distance;
calculating the spherical distance from each demand point to all demand points in other clusters according to the longitude and latitude coordinates of the demand points, and calculating the average distance;
calculating the contour coefficient of each demand point according to the average distance from each demand point to other demand points in the cluster to which the demand point belongs and the average distance from each demand point to all demand points in other clusters;
and averaging the contour coefficients of all the demand points in each cluster to obtain the contour coefficient of each cluster.
The calculation formula of the spherical distance is as follows:
wherein the content of the first and second substances,dis the spherical distance of the two demand points,ris the radius of the earth, and is,
as is the latitude of the two demand points,
the longitude of two demand points.
The calculation formula of the profile coefficient of each demand point is as follows:
wherein, the first and the second end of the pipe are connected with each other,
is as followsiThe value range of the profile coefficient of each demand point is (-1, 1);
is a firstiThe average distance from each demand point to other demand points in the cluster to which the demand point belongs, and the smaller the value, the greater the similarity degree in the cluster, the demand pointiThe greater the degree of belonging to the cluster;
is as followsiThe average distance from each demand point to all demand points in other clusters is smaller, the smaller the value is, the greater the similarity degree between clusters is, the demand point isiMay belong to other clusters;
is a firstiEach demand point going to others in other clustersjAverage distance of the individual demand points.
S3, reverse geocode matching is carried out on the single-day highway freight demand point clustering result to obtain a geographic position matching result;
in an optional embodiment of the invention, the method is based on the single-day road freight demand hot spot obtained by clustering, and a hot spot area which frequently appears in a period and has a stable large quantity of freight sources is mined from the time dimension analysis. Therefore, a batch of highway freight demand centers are determined, goods with stable and large-quantity transportation demand characteristics in the area range of the demand centers are mined, and a reference is provided for a railway transportation department to find a highway stable goods source.
Firstly, each cluster in the clustering result needs to be endowed with geographic significance, and because the clustering result of the DBSCAN has no clear clustering center, the invention calculates the average longitude and latitude of each cluster to obtain longitude and latitude coordinate pairs with the number equal to that of the clusters, utilizes a map api interface to carry out reverse geocoding query to obtain the administrative division name corresponding to each longitude and latitude coordinate pair, and approximately represents the geographic position and the coverage area of the cluster. In order to reduce the matching error between the clustering result and the geographic administrative division, the range of the address obtained by reverse geocoding query is expanded to the level of the city, county and district. And repeating the calculation and matching operation on all the data to obtain the highway freight demand center set taking days as units.
S4, mining the geographic position matching result by adopting a frequent item set mining algorithm to obtain a hot spot area required by road freight;
in an alternative embodiment of the invention, considering that the distribution of highway freight demand centers in days is not fixed, it is not reasonable to take the aggregate of all demand centers in all data as the road-rail combined transportation service object of the railway station. In order to ensure that the freight demand point can stably generate the freight demand for a long time as much as possible, the address set which frequently appears in the whole time span covered by the data needs to be found out. The invention solves the problem based on the frequent pattern mining technology, and the following relevant concept definitions are given based on the problem background:
(1) A transaction database: transactions are a subset of global items, and collections of transactions are organized into a transaction database. The set of demand center addresses for a day may be referred to herein as a transaction;
(2) Frequent item set: a set of items in the data set that frequently occur simultaneously, i.e., a set of freight demand points that frequently occur within a day over a data coverage time span;
(3) The support degree is as follows: the number of occurrences of a certain freight demand point set in the data set is proportional. When the support degree of a certain freight demand point set D is greater than the preset minimum support degree, the set D is called a frequent item set and comprisesxThe collection of items is called frequentxAn item.
The invention adopts FP-Growth algorithm to find out all Frequent item sets from the FPTree by storing data in a Frequent Pattern Tree (FPTree) and then utilizing a recursion method, and the basic flow is as follows:
(1) Scanning a data complete set, finding out a frequent 1 item set (only comprising one geographic position), and sequencing the frequent 1 item set in a descending manner according to the support degree until the support degree is equal to the minimum support degree;
(2) Scanning a data complete set, sequencing a geographical position set taking days as a unit according to the support degree calculated in the step (1), and inserting the geographical position set into a tree taking null as a root node in sequence to construct a frequent pattern tree;
(3) In the frequent pattern tree, searching a geographical position item on a prefix path of the frequent pattern tree from the residual frequent 1 item set in the step (1), constructing a conditional frequent pattern tree, recursively constructing the conditional frequent pattern tree until only one item remains in the tree structure, and arranging and combining according to the conditional frequent pattern tree to obtain all frequent item sets.
A transaction database is constructed according to the freight demand address, a day is taken as a transaction, and a frequent item set is mined by setting larger support degrees of 0.8,0.9 and 1 in consideration of the stability of the freight demand
S5, selecting alternative points of the highway-railway combined transportation hub node, constructing a hub-and-spoke type highway-railway combined transportation hub node site selection model by combining a highway freight demand hot spot area, and solving to obtain a highway-railway combined transportation hub point and a service area thereof;
in an optional embodiment of the invention, on the basis of the identification result of the demand hot spot region, the alternative selection points of the highway-railway transport hub are selected, a highway-railway transport site selection distribution model is constructed and solved, and finally the service region, the main goods and the development emphasis point of each highway-railway transport hub are determined.
In order to screen out stations with the construction conditions and the operation capacity of the highway-railway combined transportation hub, the invention sets the following screening rules:
(1) And (4) station grade. The railway station grade approval method provides three indexes of getting on and off the train on a daily basis, changing the number of passengers into the number of passengers, forwarding packages in the forwarding process, the number of loading and unloading vehicles on a daily basis and the number of dispatching vehicles on a daily basis to evaluate the station grade, so that the station grade directly indicates the transport capacity of the station. Stations other than the special station and the first-class station are not considered;
(2) The station geographical location. The special stations and the first-class stations are built in the main urban area due to passenger transport requirements, do not have the condition of building hubs, and simultaneously consider the requirement of relieving the non-capital function of Beijing. Deleting stations such as Beijing, beijing West station, tianjin West station and the like;
(3) And (4) station operation range. According to the public data of the Chinese railway 95306 website, the handling of the transportation business of the railway freight yard is limited by partial stations, for example, the stone mountain station only handles the steel transportation business, and stations which are built based on industrial and mining enterprises and have narrow transportation business surfaces are excluded.
The multi-type intermodal network is constructed according to the radial principle, so that the logistics cost can be effectively reduced, and the transportation efficiency is improved. The scattered small-batch cargo flows of road freight are concentrated on a large highway-railway combined transportation hub through short-distance highway transportation, and then middle-distance and long-distance trunk transportation is carried out by utilizing railways, so that the advantages of various transportation modes are fully utilized, and the large-scale economic benefit of transportation is generated. The mode of the combined transportation of the highway and the railway designed by the invention is shown in figure 3.
Based on the highway-railway combined transportation mode, the longitude and latitude coordinates of the highway transportation demand point and the alternative highway-railway combined transportation pivot point, the demand quantity of the highway demand point and the fixed cost and the maximum capacity of the alternative highway-railway combined transportation pivot point are known. The method aims to construct a site selection and distribution model, select sites of highway and railway transportation pivot points, and make a decision on highway transportation demand points served by each pivot point so as to find a site selection and distribution scheme with the minimum total cost (construction cost and transportation cost) and meet regional freight requirements.
In order to improve the modeling and solving efficiency, the invention simplifies the process of actual road-rail transport site selection and distribution, and makes the following assumptions:
(1) The short-distance road transportation distance is measured and calculated by utilizing road network data, but the redundant transportation distance caused by road conditions and driver behaviors is not considered;
(2) The requirement of each road transportation demand point can be met by only one pivot point, and the behavior of transportation to a plurality of pivot points for scattered intermodal transportation does not exist;
(3) Only the fixed cost and the transit loading and unloading cost of the pivot point of the construction intermodal transport are considered, and other operation costs are not considered.
The invention discloses a method for constructing a hub node site selection model of a hub-spoke type highway-railway combined transport hub, which comprises the following steps:
the method comprises the steps of constructing a hub and rail transport hub node addressing model by taking the minimum of highway and railway transport construction operation cost and transport cost as an objective function, taking the maximum number of construction pivot point points, the requirement of each highway transport requirement point is met by one transport pivot point, the transport volume of the requirement points does not exceed the total requirement quantity of the requirement points, all transport requirements of the requirement points are met, the transport volume of the transport pivot point is equal to the sum of the transport volume of each requirement point to the pivot point, the total collection transport volume of selected transport pivot alternative points does not exceed the maximum transport capacity of the transport pivot point, whether the pivot points provide transport services for the requirement points or not, whether alternative pivot point is selected, the quantity of goods transported from the requirement points to another requirement point and the quantity of goods transported to the pivot points as constraint conditions.
The hub and spoke type road and rail transport hub node site selection model constructed by the invention specifically comprises the following steps:
s.t.
wherein, the first and the second end of the pipe are connected with each other,
is a set of alternative points of the highway-railway combined transportation hub,
the cost is fixed for the construction of the hub,fthe loading and unloading cost is transferred in unit cargo quantity of the alternative pivot point,
for selection point of road-rail transport hubjMaximum ofThe capability of transportation is realized by the device,
a 0-1 decision variable for whether the alternative pivot point is selected,
is a set of demand points for road transport,
for highway transportation demand pointsiAlternative points to highway-railway combined transport hubjThe amount of cargo to be transported is,
for selection point of road-rail transport hubjWhether to the highway transportation demand pointiA 0-1 decision variable for providing an intermodal service,
for highway transportation demand pointsiAlternative point of road-rail transport hubjThe distance between the spherical surface of the optical fiber,Cunit transportation cost;Sthe maximum number of the pivot points is constructed,
for highway transportation demand pointsiThe total volume of traffic of (a) is,
for transportation to alternate points of road-rail transport hubjThe amount of cargo.
S6, excavating freight characteristics in the service area of each link point of the highway-railway combined transportation pivot to obtain the service cargo type of each link point of the highway-railway combined transportation pivot;
in an optional embodiment of the invention, after the key hub and the service range thereof are determined, the freight characteristics in the area range are mined, so that the service cargo type can be comprehensively considered, the highway-railway combined transportation volume can be more comprehensively predicted, and the prediction accuracy is improved.
As the railway department mainly pays attention to the characteristics of stable and large-batch transportation of the goods and the goods quantity of the scattered goods, the method carries out statistics of the transportation quantity of the goods in the class of the goods distributed to the road transportation in the service range of each junction point; in addition, as the economic benefit of railway transportation on scale cannot be effectively reflected in short-distance transportation, the average distance characteristic of the goods in the region range needs to be calculated. And screening road cargo sources suitable for the intermodal operation at the pivot point by combining the classified freight volume and the average freight distance.
The rules for screening the proper sources are set as follows:
(1) The weight of each single cargo is recorded in detail by the original data, abnormal data are deleted, and the cargo with the single daily freight volume ratio larger than 10% is primarily screened out;
(2) And calculating the average road transportation distance according to the goods, and calculating the median of the goods-classified transportation in order to prevent the interference of the extreme value of the transportation distance data. And setting the goods as the alternative goods type when the average distance is more than 500 kilometers and the average distance of the goods is more than the median of the distance. It means that the majority of the sources of the cargo over a distance of 500 km, such cargo is considered suitable for rail transport.
And establishing a query statement in the MySQL database according to the rules, and screening the regional cargo types. The result of the main cargo type can be obtained, wherein the primary cargo types are arranged according to the proportion order of the cargo quantity.
And S7, extracting the cargo quantity of each type of service cargo of each highway-railway combined transportation pivot point from the highway cargo source data, and predicting the cargo quantity at the next moment by adopting an integrated sliding average autoregressive model.
In an alternative embodiment of the present invention, the integrated moving average autoregressive model prediction employed by the present invention integrates the autoregressive term AR and the moving average term MA to predict the current, namely:
wherein
Is a constant，
Is a white noise sequence and is a white noise sequence,
is one
An autoregressive model of order, considering the current date as equal to the sum of the weighted average of the historical data and an error disturbance term,
is one
And (4) a moving average model of the order, wherein the current date is considered to be equal to the sum of the weighted average of the historical error disturbance and the historical mean.
Form a
Model, wherein
Is the difference order. The model considers that current date data is related to both historical data and historical errors.
The method is based on the integrated sliding average autoregressive model, and the cargo quantity of each type of service cargo at each highway-railway combined transportation pivot point at the next moment is obtained according to the cargo quantity prediction of each type of service cargo at each highway-railway combined transportation pivot point.
Example 2
As shown in fig. 4, an embodiment of the present invention provides a device for predicting an amount of transportation of a utility grid and a railway based on the method for predicting an amount of transportation of a utility grid and a railway described in embodiment 1, including:
the data preprocessing module is used for acquiring the goods source data of the highway and preprocessing the data;
the data clustering module is used for clustering the preprocessed road freight source data by adopting a DBSCAN clustering algorithm based on an optimal contour coefficient to obtain a single-day road freight demand point clustering result;
the data matching module is used for carrying out reverse geocode matching on the clustering result of the single-day highway freight demand points to obtain a geographic position matching result;
the data mining module is used for mining the geographic position matching result by adopting a frequent item set mining algorithm to obtain a highway freight requirement hot spot area;
the model building module is used for selecting alternative points of the highway-railway combined transportation hub nodes, building a hub-spoke type highway-railway combined transportation hub node site selection model by combining with a highway freight demand hot spot area, and solving to obtain the highway-railway combined transportation hub points and service areas thereof;
the freight characteristic mining module is used for mining freight characteristics in the service area of each highway-railway combined transportation pivot point to obtain the service cargo type of each highway-railway combined transportation pivot point;
and the cargo quantity prediction module is used for extracting the cargo quantities of various types of service cargos at the highway and railway transportation pivot points from the highway cargo source data and predicting the cargo quantity at the next moment by adopting an integrated moving average autoregressive model.
The device for predicting the combined transportation capacity of the highway and railway provided by the embodiment 2 of the invention has the beneficial effect of the method for predicting the combined transportation capacity of the highway and railway in the embodiment 1.
Example 3
The embodiment of the invention provides a prediction device for the combined transportation volume of a highway and a railway based on the prediction method for the combined transportation volume of the highway and the railway described in the embodiment 1, which comprises the following steps: a memory and a processor;
the memory is used for storing programs;
the processor is used for executing the program to realize the steps of the method for predicting the combined transportation capacity of the highway and railway.
The device for predicting the transport capacity of the highway and railway provided by the embodiment 3 of the invention has the beneficial effect of the method for predicting the transport capacity of the highway and railway in the embodiment 1.
Example 4
An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, where the computer program is executed by a processor, and the computer program implements the steps of the method for predicting the transportation volume of a highway and a railway as described in embodiment 1.
Embodiment 4 of the present invention provides a computer-readable storage medium, which has the beneficial effect of the method for predicting the combined transportation volume of the highway and railway in embodiment 1.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art, having the benefit of this disclosure, may effect numerous modifications thereto and changes may be made without departing from the scope of the invention in its aspects.
Claims (10)
1. A method for predicting the transportation capacity of the highway-railway combined transport is characterized by comprising the following steps:
s1, obtaining road goods source data and preprocessing the data;
s2, clustering the preprocessed road cargo source data by adopting a DBSCAN clustering algorithm based on an optimal contour coefficient to obtain a clustering result of the single-day road cargo demand points;
s3, reverse geocode matching is carried out on the single-day highway freight demand point clustering result to obtain a geographic position matching result;
s4, mining the geographic position matching result by adopting a frequent item set mining algorithm to obtain a hot spot area required by road freight;
s5, selecting alternate points of the highway-railway combined transportation hub nodes, constructing a hub-spoke type highway-railway combined transportation hub node site selection model by combining with a highway freight demand hot spot area, and solving to obtain the highway-railway combined transportation hub points and service areas thereof;
s6, excavating freight characteristics in the service area of each highway-railway combined transportation pivot point to obtain the service cargo type of each highway-railway combined transportation pivot point;
and S7, extracting the cargo quantity of each type of service cargo of each highway-railway combined transportation pivot point from the highway cargo source data, and predicting the cargo quantity at the next moment by adopting an integrated sliding average autoregressive model.
2. The method for predicting the transportation volume of the highway-railway combined transport according to claim 1, wherein the step S2 specifically comprises the following substeps:
s21, setting a value range and a transformation step length of an initial neighborhood radius and a minimum point number;
s22, randomly selecting an initial demand point from the demand set points;
s23, judging whether the demand point with the minimum point number exists in the neighborhood radius range of the demand point;
if yes, clustering all the existing demand points into a cluster, and jumping to the step S24;
otherwise, judging the demand point as a noise point, and skipping to the step S22;
s24, traversing each demand point in the cluster, and judging whether the demand point with the minimum point number exists in the neighborhood radius range of the selected demand point;
if yes, merging all the existing demand points into the cluster, and jumping to the step S25;
otherwise, directly jumping to the step S25;
s25, judging whether the non-traversed demand points exist in the cluster;
if yes, jumping to the step S24;
otherwise, jumping to step S26;
s26, judging whether unprocessed demand points exist in the demand set points;
if yes, jumping to step S22;
otherwise, jumping to step S27;
s27, judging whether the neighborhood radius and the minimum point number reach the maximum value or not;
if yes, jumping to step S28;
otherwise, updating the neighborhood radius and the minimum point number according to the conversion step length, and jumping to the step S22;
s28, calculating the contour coefficient of each cluster and marking cluster marks to obtain the longitude and latitude coordinates of each demand point and the cluster label of each demand point.
3. The method for predicting road-rail transport capacity according to claim 2, wherein the contour coefficient of each cluster is calculated by:
calculating the spherical distance from each demand point to other demand points in the cluster to which the demand point belongs according to the longitude and latitude coordinates of the demand points, and calculating the average distance;
calculating the spherical distance from each demand point to all demand points in other clusters according to the longitude and latitude coordinates of the demand points, and calculating the average distance;
calculating the contour coefficient of each demand point according to the average distance from each demand point to other demand points in the cluster to which the demand point belongs and the average distance from each demand point to all demand points in other clusters;
and averaging the contour coefficients of all the demand points in each cluster to obtain the contour coefficient of each cluster.
4. The method for predicting road-rail transport capacity according to claim 3, wherein the calculation formula of the spherical distance is as follows:
wherein the content of the first and second substances,dis the spherical distance of the two required points,rwhich is the radius of the earth, is,
as is the latitude of the two demand points,
the longitude of two demand points.
5. The method for predicting the road-rail transport capacity according to claim 3, wherein the profile coefficient of each demand point is calculated by the formula:
wherein, the first and the second end of the pipe are connected with each other,
is a firstiThe contour factor of each of the demand points is,
is a firstiThe average distance of a demand point to other demand points within the cluster to which it belongs,
is as followsiThe minimum average distance from each demand point to all demand points in other clusters;
is as followsiEach demand point going to others in other clustersjAverage distance of the individual demand points.
6. The method for forecasting combined transportation capacity of a highway and railway according to claim 1, wherein the method for constructing the hub and spoke type combined transportation hub node addressing model comprises the following steps:
the method comprises the steps of constructing a hub and rail transport hub node addressing model by taking the minimum of highway and railway transport construction operation cost and transport cost as an objective function, taking the maximum number of construction pivot point points, the requirement of each highway transport requirement point is met by one transport pivot point, the transport volume of the requirement points does not exceed the total requirement quantity of the requirement points, all transport requirements of the requirement points are met, the transport volume of the transport pivot point is equal to the sum of the transport volume of each requirement point to the pivot point, the total collection transport volume of selected transport pivot alternative points does not exceed the maximum transport capacity of the transport pivot point, whether the pivot points provide transport services for the requirement points or not, whether alternative pivot point is selected, the quantity of goods transported from the requirement points to another requirement point and the quantity of goods transported to the pivot points as constraint conditions.
7. The method for predicting the transportation capacity of the highway-railway combined transportation according to claim 6, wherein the hub-and-spoke type highway-railway combined transportation hub node location model specifically comprises:
s.t.
wherein the content of the first and second substances,
is a highway-railway transport hub alternate point set,
the cost is fixed for the construction of the hub,ffor the transfer of the handling charges per unit of cargo volume at the alternative pivot point,
for selecting points for highway-railway combined transportation hubjThe maximum transport capacity of the transport means (c),
a 0-1 decision variable for whether the alternative pivot point is selected,
is a set of demand points for road transport,
for highway transportation demand pointsiAlternative point of road-rail transport hubjThe amount of cargo to be transported is,
is a highway-railway unionAlternative point of operation hubjWhether to the highway transportation demand pointiA 0-1 decision variable for providing intermodal services,
for highway transportation demand pointsiAlternative point of road-rail transport hubjThe spherical distance of (a) is greater than (b),Cunit transportation cost;Sthe maximum number of the pivot points is constructed,
for highway transportation demand pointsiThe total volume of traffic of (2) is,
for transportation to alternate points of road-rail transport hubjThe amount of cargo.
8. An inter-highway/railway transportation volume prediction device, comprising:
the data preprocessing module is used for acquiring the goods source data of the highway and preprocessing the data;
the data clustering module is used for clustering the preprocessed road freight source data by adopting a DBSCAN clustering algorithm based on an optimal contour coefficient to obtain a single-day road freight demand point clustering result;
the data matching module is used for carrying out reverse geocode matching on the clustering result of the single-day highway freight demand points to obtain a geographic position matching result;
the data mining module is used for mining the geographic position matching result by adopting a frequent item set mining algorithm to obtain a hot spot area required by road freight;
the model building module is used for selecting alternative points of the highway-railway combined transportation hub nodes, building a hub-spoke type highway-railway combined transportation hub node site selection model by combining with a highway freight demand hot spot area, and solving to obtain the highway-railway combined transportation hub points and service areas thereof;
the freight characteristic mining module is used for mining freight characteristics in the service area of each highway-railway combined transportation pivot point to obtain the service cargo type of each highway-railway combined transportation pivot point;
and the cargo quantity prediction module is used for extracting the cargo quantities of various types of service cargos at the highway and railway transportation pivot points from the highway cargo source data and predicting the cargo quantity at the next moment by adopting an integrated moving average autoregressive model.
9. An inter-highway/railway transportation volume prediction apparatus, comprising: a memory and a processor;
the memory is used for storing programs;
the processor, configured to execute the program, and implement the steps of the method for predicting the quantity of intermodal transportation of highway and railway according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the highway to railway freight prediction method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211169408.2A CN115271260B (en) | 2022-09-26 | 2022-09-26 | Road-rail transport capacity prediction method, device, equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211169408.2A CN115271260B (en) | 2022-09-26 | 2022-09-26 | Road-rail transport capacity prediction method, device, equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115271260A true CN115271260A (en) | 2022-11-01 |
| CN115271260B CN115271260B (en) | 2022-12-13 |
Family
ID=83756385
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211169408.2A Active CN115271260B (en) | 2022-09-26 | 2022-09-26 | Road-rail transport capacity prediction method, device, equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115271260B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012071944A (en) * | 2010-09-29 | 2012-04-12 | Railway Technical Research Institute | Method for estimating section freight traffic volume in railroad trunk line section |
| JP2014002466A (en) * | 2012-06-15 | 2014-01-09 | Railway Technical Research Institute | Two-step analysis method for measuring railroad demand in land freight |
| CN104992244A (en) * | 2015-07-08 | 2015-10-21 | 中国民用航空总局第二研究所 | Airport freight traffic prediction analysis method based on SARIMA and RBF neural network integration combination model |
| CN110516869A (en) * | 2019-08-22 | 2019-11-29 | 西南交通大学 | A kind of combined airway railway traffic hinge network plan method for shipping field |
| CN111915099A (en) * | 2020-08-17 | 2020-11-10 | 北京交通大学 | Freight volume prediction system with visual freight information |
| CN111967828A (en) * | 2020-08-20 | 2020-11-20 | 北京交通大学 | Whole-process logistics-oriented road-rail combined transport product collaborative optimization method |
| CN114298419A (en) * | 2021-12-30 | 2022-04-08 | 北京世纪超越管理咨询服务有限公司 | Multi-type intermodal transportation planning method and device, electronic equipment and storage medium |
| CN114676868A (en) * | 2020-12-24 | 2022-06-28 | 深圳顺丰快运科技有限公司 | Logistics cargo quantity prediction method and device, computer equipment and storage medium |
| CN114997787A (en) * | 2022-06-08 | 2022-09-02 | 西南交通大学 | Railway freight competitive pricing method based on freight rate prediction |
-
2022
- 2022-09-26 CN CN202211169408.2A patent/CN115271260B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012071944A (en) * | 2010-09-29 | 2012-04-12 | Railway Technical Research Institute | Method for estimating section freight traffic volume in railroad trunk line section |
| JP2014002466A (en) * | 2012-06-15 | 2014-01-09 | Railway Technical Research Institute | Two-step analysis method for measuring railroad demand in land freight |
| CN104992244A (en) * | 2015-07-08 | 2015-10-21 | 中国民用航空总局第二研究所 | Airport freight traffic prediction analysis method based on SARIMA and RBF neural network integration combination model |
| CN110516869A (en) * | 2019-08-22 | 2019-11-29 | 西南交通大学 | A kind of combined airway railway traffic hinge network plan method for shipping field |
| CN111915099A (en) * | 2020-08-17 | 2020-11-10 | 北京交通大学 | Freight volume prediction system with visual freight information |
| CN111967828A (en) * | 2020-08-20 | 2020-11-20 | 北京交通大学 | Whole-process logistics-oriented road-rail combined transport product collaborative optimization method |
| CN114676868A (en) * | 2020-12-24 | 2022-06-28 | 深圳顺丰快运科技有限公司 | Logistics cargo quantity prediction method and device, computer equipment and storage medium |
| CN114298419A (en) * | 2021-12-30 | 2022-04-08 | 北京世纪超越管理咨询服务有限公司 | Multi-type intermodal transportation planning method and device, electronic equipment and storage medium |
| CN114997787A (en) * | 2022-06-08 | 2022-09-02 | 西南交通大学 | Railway freight competitive pricing method based on freight rate prediction |
Non-Patent Citations (17)
| Title |
|---|
| HYEOK HEO;: "A Study on the Factors Development of the Prediction Model of Freight Volume Using the Deep Learning", 《2022 IEEE/ACIS 7TH INTERNATIONAL CONFERENCE ON BIG DATA, CLOUD COMPUTING, AND DATA SCIENCE (BCD)》 * |
| NIKOLAOS SERVOS, XIAODI LIU, MICHAEL TEUCKE, MICHAEL FREITAG: "Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms", 《LOGISTICS》 * |
| PRASAD C MAHAJAN; ARVIND W KIWELEKAR; LAXMAN D NETAK; AKSHAY GHO: "Applications of Predictive Analytics in Marine Transportation", 《 2021 INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND GREEN ENGINEERING (CCGE)》 * |
| YANNI JU；HAIHUA YUAN；ZONGPING LI；MI GAN: "Multilayer structures and resilience evaluation for multimode regional rail transit system", 《IET INTELLIGENT TRANSPORT SYSTEMS》 * |
| 何西健: "胶州铁路物流中心运量预测研究", 《铁道运输与经济》 * |
| 刘梦婷等: "基于SARIMA模型的我国铁路货运量预测分析", 《贵州师范学院学报》 * |
| 唐继孟等: "公铁联运规模效应和枢纽拥挤效应的联动分析", 《交通运输***工程与信息》 * |
| 左小雨等: "基于灰色预测模型对我国铁路货运量的预测", 《物流科技》 * |
| 张生瑞梁营力: "基于公铁联运综合交通枢纽的交通需求预测理论研究", 《 2007第三届中国智能交通年会论文集》 * |
| 曹菁菁等: "基于并行Apriori的物流路径频繁模式研究", 《计算机工程与应用》 * |
| 李孟良等: "公铁联运网络的枢纽选址模型及算法", 《铁道学报》 * |
| 王蕊: "不确定条件下轴辐式公铁联运网络设计与优化", 《中国优秀硕士学位论文全文数据库 (经济与管理科学辑)》 * |
| 罗莉: "土地利用模型在运量预测中的研究", 《交通标准化》 * |
| 蒋大成: "基于数据挖掘的农产品物流配送中心选址研究", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》 * |
| 钱秋君: "基于货车轨迹数据的运力预测研究", 《中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑)》 * |
| 颜阳等: "基于灰色线性回归模型的哈尔滨铁路枢纽货运量预测研究", 《铁道货运》 * |
| 黄娟；贺政纲: "基于Logit模型的空铁联运运量预测", 《交通运输工程与信息学报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115271260B (en) | 2022-12-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Taniguchi et al. | Modelling city logistics using recent innovative technologies | |
| CN101325004B (en) | Method for compensating real time traffic information data | |
| Taniguchi et al. | Predicting the effects of city logistics schemes | |
| Gan et al. | The identification of truck-related greenhouse gas emissions and critical impact factors in an urban logistics network | |
| CN106448132A (en) | Conventional public traffic service index real-time evaluation system and method | |
| CN110599765A (en) | Road passenger and cargo transportation volume index statistical method based on multi-source data fusion | |
| CN107506433A (en) | Urban development space general layout Scene Simulation system | |
| Ambrosini et al. | Urban freight modelling: a review | |
| CN113222271A (en) | Medium and small airport site selection layout method under comprehensive transportation system | |
| You | Methodology for tour-based truck demand modeling | |
| Ranathunga et al. | Simulation-based efficiency assessment of integrated first-mile pickup and last-mile delivery in an e-commerce logistics network | |
| Gu et al. | Enhancing market service and enterprise operations through a large-scale GIS-based distribution system | |
| Cheng et al. | Design of a support system for complicated logistics location integrating big data | |
| CN115271260B (en) | Road-rail transport capacity prediction method, device, equipment and readable storage medium | |
| Hua et al. | Large-scale dockless bike sharing repositioning considering future usage and workload balance | |
| Kumar et al. | Urban Freight Regulations: How much they cost the consumers? | |
| Hu et al. | Reliable design of urban surface-underground integrated logistics system network with stochastic demand and social-environmental concern | |
| Yang et al. | GIS‐Based Analysis of Railway’s Origin/Destination Path‐Selecting Behavior | |
| Sharman | Behavioural modelling of urban freight transportation: Activity and inter-arrival duration models estimated using GPS data | |
| Tsamboulas et al. | Methodology for estimating freight volume shift in an international intermodal corridor | |
| Akter | Truck activity pattern classification using anonymous mobile sensor data | |
| Thompson et al. | Optimizing Courier Routes in Central Business Districts | |
| Kang | Unraveling Decentralization of Warehousing and Distribution Centers: Three Essays | |
| Prato et al. | Characterization of Freight Transportation in Colombia Using the National Registry of Cargo Dispatches (RNDC) | |
| Rabe et al. | Evaluating the Benefits of Collaborative Distribution with Supply Chain Simulation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CB03 | Change of inventor or designer information |
Inventor after: Qian Qiujun Inventor after: Gan Mi Inventor after: Ou Qichen Inventor after: Huang Xi Inventor after: Zhang Lu Inventor after: Feng Yunxia Inventor after: Wang Yichen Inventor before: Qian Qiujun Inventor before: Gan Mi Inventor before: Ou Qichen Inventor before: Huang Xi Inventor before: Zhang Lu Inventor before: Feng Yunxia Inventor before: Wang Yichen |
|
| CB03 | Change of inventor or designer information |